Abstract: Double-diffusive natural convection in an open top
square cavity and heated from the side is studied numerically.
Constant temperatures and concentration are imposed along the right
and left walls while the heat balance at the surface is assumed to obey
Newton-s law of cooling. The finite difference method is used to
solve the dimensionless governing equations. The numerical results
are reported for the effect of Marangoni number, Biot number and
Prandtl number on the contours of streamlines, temperature and
concentration. The predicted results for the average Nusselt number
and Sherwood number are presented for various parametric
conditions. The parameters involved are as follows; the thermal
Marangoni number, 0 ≤ MaT ≤1000 , the solutal Marangoni number,
0 1000 c ≤ Ma ≤ , the Biot number, 0 ≤ Bi ≤ 6 , Grashof number,
5 Gr = 10 and aspect ratio 1. The study focused on both flows; thermal
dominated, N = 0.8 , and compositional dominated, N = 1.3 .
Abstract: Prior research has not effectively investigated how the
profitability of Chinese branches affect FDIs in China [1, 2], so this
study for the first time incorporates realistic earnings information
to systematically investigate effects of innovation, imitation, and
profit factors of FDI diffusions from Taiwan to China. Our nonlinear
least square (NLS) model, which incorporates earnings factors,
forms a nonlinear ordinary differential equation (ODE) in numerical
simulation programs. The model parameters are obtained through
a genetic algorithms (GA) technique and then optimized with the
collected data for the best accuracy. Particularly, Taiwanese regulatory
FDI restrictions are also considered in our modified model to meet
the realistic conditions. To validate the model-s effectiveness, this
investigation compares the prediction accuracy of modified model
with the conventional diffusion model, which does not take account
of the profitability factors.
The results clearly demonstrate the internal influence to be positive,
as early FDI adopters- consistent praises of FDI attract potential firms
to make the same move. The former erects a behavior model for the
latter to imitate their foreign investment decision. Particularly, the
results of modified diffusion models show that the earnings from
Chinese branches are positively related to the internal influence. In
general, the imitating tendency of potential consumers is substantially
hindered by the losses in the Chinese branches, and these firms would
invest less into China. The FDI inflow extension depends on earnings
of Chinese branches, and companies will adjust their FDI strategies
based on the returns. Since this research has proved that earning is
an influential factor on FDI dynamics, our revised model explicitly
performs superior in prediction ability than conventional diffusion
model.
Abstract: Carbon nanotubes (CNTs) possess unique structural,
mechanical, thermal and electronic properties, and have been
proposed to be used for applications in many fields. However, to
reach the full potential of the CNTs, many problems still need to be
solved, including the development of an easy and effective
purification procedure, since synthesized CNTs contain impurities,
such as amorphous carbon, carbon nanoparticles and metal particles.
Different purification methods yield different CNT characteristics
and may be suitable for the production of different types of CNTs. In
this study, the effect of different purification chemicals on carbon
nanotube quality was investigated. CNTs were firstly synthesized by
chemical vapor deposition (CVD) of acetylene (C2H2) on a
magnesium oxide (MgO) powder impregnated with an iron nitrate
(Fe(NO3)3·9H2O) solution. The synthesis parameters were selected
as: the synthesis temperature of 800°C, the iron content in the
precursor of 5% and the synthesis time of 30 min. The liquid phase
oxidation method was applied for the purification of the synthesized
CNT materials. Three different acid chemicals (HNO3, H2SO4, and
HCl) were used in the removal of the metal catalysts from the
synthesized CNT material to investigate the possible effects of each
acid solution to the purification step. Purification experiments were
carried out at two different temperatures (75 and 120 °C), two
different acid concentrations (3 and 6 M) and for three different time
intervals (6, 8 and 15 h). A 30% H2O2 : 3M HCl (1:1 v%) solution
was also used in the purification step to remove both the metal
catalysts and the amorphous carbon. The purifications using this
solution were performed at the temperature of 75°C for 8 hours.
Purification efficiencies at different conditions were evaluated by
thermogravimetric analysis. Thermal and electrical properties of
CNTs were also determined. It was found that the obtained electrical
conductivity values for the carbon nanotubes were typical for organic
semiconductor materials and thermal stabilities were changed
depending on the purification chemicals.
Abstract: In recent five decades, textured yarns of polyester fiber produced by false twist method are the most
important and mass-produced manmade fibers. There are
many parameters of cross section which affect the physical and mechanical properties of textured yarns. These parameters
are surface area, perimeter, equivalent diameter, large
diameter, small diameter, convexity, stiffness, eccentricity, and hydraulic diameter. These parameters were evaluated by
digital image processing techniques. To find trends between production criteria and evaluated parameters of cross section, three criteria of production line have been adjusted and different types of yarns were produced. These criteria are
temperature, drafting ratio, and D/Y ratio. Finally the relations between production criteria and cross section parameters were
considered. The results showed that the presented technique can recognize and measure the parameters of fiber cross section in acceptable accuracy. Also, the optimum condition
of adjustments has been estimated from results of image analysis evaluation.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
three feature selection methods are evaluated: Random Selection,
Information Gain (IG) and Support Vector Machine feature selection
(called SVM_FS). We show that the best results were obtained with
SVM_FS method for a relatively small dimension of the feature
vector. Also we present a novel method to better correlate SVM
kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: Power system stability enhancement by simultaneous tuning of a Power System Stabilizer (PSS) and a Static Var Compensator (SVC)-based controller is thoroughly investigated in this paper. The coordination among the proposed damping stabilizers and the SVC internal voltage regulators has also been taken into consideration. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and Real-Coded Genetic Algorithm (RCGA) is employed to search for optimal controller parameters. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance and unbalanced fault conditions.
Abstract: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
Abstract: Artificial Neural Network (ANN)s are best suited for
prediction and optimization problems. Trained ANNs have found
wide spread acceptance in several antenna design systems. Four
parameters namely antenna radiation resistance, loss resistance, efficiency,
and inductance can be used to design an antenna layout though
there are several other parameters available. An ANN can be trained
to provide the best and worst case precisions of an antenna design
problem defined by these four parameters. This work describes the
use of an ANN to generate the four mentioned parameters for a loop
antenna for the specified frequency range. It also provides insights
to the prediction of best and worst-case design problems observed
in applications and thereby formulate a model for physical layout
design of a loop antenna.
Abstract: Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control are discussed in this paper. Soft computing technique – Fuzzy logic is applied in this paper for the speed control of induction motor to achieve maximum torque with minimum loss. The fuzzy logic controller is implemented using the Field Oriented Control technique as it provides better control of motor torque with high dynamic performance. The motor model is designed and membership functions are chosen according to the parameters of the motor model. The simulated design is tested using various tool boxes in MATLAB. The result concludes that the efficiency and reliability of the proposed speed controller is good.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: The paper represents a reflection on how to select proper indicators to assess the progress of regional contexts towards a knowledge-based society. Taking the first research methodologies elaborated at an international level (World Bank, OECD, etc.) as a reference point, this work intends to identify a set of indicators of the knowledge economy suitable to adequately understand in which manner and to which extent the territorial development dynamics are correlated with the knowledge-base of the considered local society. After a critical survey of the variables utilized within other approaches adopted by international or national organizations, this paper seeks to elaborate a framework of variables, named Regional Knowledge Economy Indicators (ReKEI), necessary to describe the knowledge-based relations of subnational socio-economic contexts. The realization of this framework has a double purpose: an analytical one consisting in highlighting the regional differences in the governance of knowledge based processes, and an operative one consisting in providing some reference parameters for contributing to increasing the effectiveness of those economic policies aiming at enlarging the knowledge bases of local societies.
Abstract: In this paper, determining the optimal proportionalintegral-
derivative (PID) controller gains of an single-area load
frequency control (LFC) system using genetic algorithm (GA) is
presented. The LFC is notoriously difficult to control optimally using
conventionally tuning a PID controller because the system parameters
are constantly changing. It is for this reason the GA as tuning strategy
was applied. The simulation has been conducted in MATLAB
Simulink package for single area power system. the simulation results
shows the effectiveness performance of under various disturbance.
Abstract: Tool wear and surface roughness prediction plays a
significant role in machining industry for proper planning and control
of machining parameters and optimization of cutting conditions. This
paper deals with developing an artificial neural network (ANN)
model as a function of cutting parameters in turning steel under
minimum quantity lubrication (MQL). A feed-forward
backpropagation network with twenty five hidden neurons has been
selected as the optimum network. The co-efficient of determination
(R2) between model predictions and experimental values are 0.9915,
0.9906, 0.9761 and 0.9627 in terms of VB, VM, VS and Ra
respectively. The results imply that the model can be used easily to
forecast tool wear and surface roughness in response to cutting
parameters.
Abstract: The purpose of our study was to compare spontaneous
re-epithelisation characteristics versus assisted re-epithelisation. In
order to assess re-epithelisation of the injured skin, we have imagined
and designed a burn wound model on Wistar rat skin. Our aim was to
create standardised, easy reproducible and quantifiable skin lesions
involving entire epidermis and superficial dermis. We then have
applied the above mentioned therapeutic strategies to compare
regeneration of epidermis and dermis, local and systemic parameter
changes in different conditions. We have enhanced the reepithelisation
process under a moist atmosphere of a polyurethane
wound dress modified with helium non-thermal plasma, and with the
aid of direct cold-plasma treatment respectively. We have followed
systemic parameters change: hematologic and biochemical
parameters, and local features: oxidative stress markers and histology
of skin in the above mentioned conditions. Re-epithelisation is just a
part of the skin regeneration process, which recruits cellular
components, with the aid of epidermal and dermal interaction via
signal molecules.
Abstract: Risk Assessment Tool (RAT) is an expert system that
assesses, monitors, and gives preliminary treatments automatically
based on the project plan. In this paper, a review was taken out for
the current project time management risk assessment tools for SME
software development projects, analyze risk assessment parameters,
conditions, scenarios, and finally propose risk assessment tool (RAT)
model to assess, treat, and monitor risks. An implementation prototype
system is developed to validate the model.
Abstract: Analyses carried out on examples of detected defects
echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect.
This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of
this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis
(PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a
volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the
various algorithms proposed in this study.
Abstract: This paper presents probabilistic horizontal seismic
hazard assessment of Naghan, Iran. It displays the probabilistic
estimate of Peak Ground Horizontal Acceleration (PGHA) for the
return period of 475, 950 and 2475 years. The output of the
probabilistic seismic hazard analysis is based on peak ground
acceleration (PGA), which is the most common criterion in designing
of buildings. A catalogue of seismic events that includes both
historical and instrumental events was developed and covers the
period from 840 to 2009. The seismic sources that affect the hazard
in Naghan were identified within the radius of 200 km and the
recurrence relationships of these sources were generated by Kijko
and Sellevoll. Finally Peak Ground Horizontal Acceleration (PGHA)
has been prepared to indicate the earthquake hazard of Naghan for
different hazard levels by using SEISRISK III software.
Abstract: The medical studies often require different methods
for parameters selection, as a second step of processing, after the
database-s designing and filling with information. One common
task is the selection of fields that act as risk factors using wellknown
methods, in order to find the most relevant risk factors and
to establish a possible hierarchy between them. Different methods
are available in this purpose, one of the most known being the
binary logistic regression. We will present the mathematical
principles of this method and a practical example of using it in the
analysis of the influence of 10 different psychiatric diagnostics
over 4 different types of offences (in a database made from 289
psychiatric patients involved in different types of offences).
Finally, we will make some observations about the relation
between the risk factors hierarchy established through binary
logistic regression and the individual risks, as well as the results of
Chi-squared test. We will show that the hierarchy built using the
binary logistic regression doesn-t agree with the direct order of risk
factors, even if it was naturally to assume this hypothesis as being
always true.
Abstract: A CFD software was employed to analyze the
characteristics of the flat round porous aerostatic bearings. The effects
of gap between the bearing and the guide way and the porosity of the
porous material on the load capacity of the bearing were studied. The
adequacy of the simulation model and the approach was verified. From
the parametric study, it is found that the depth of the flow path does not
influence the load capacity of the bearing; the load capacity of the
bearing will decrease if the thickness of the porous material increases
or the porous material protrudes above the bearing housing; the
variation of the chamfer at the edge of the bearing does not affect the
bearing load capacity. For a bearing with an air gap of 5μm and a
porosity of 0.1, the average load capacity and the pressure distribution
of the bearing are nearly unchanged no matter the bearing moves at a
constant or a varying speed.
Abstract: The Chiu-s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering principle to estimate both the number of clusters and the initial locations of a cluster centers. Each obtained cluster corresponds to a Mamdani fuzzy rule. Then, it optimizes the fuzzy model parameters by applying a genetic algorithm. This method is illustrated on a traffic network management application. We suggest also a Mamdani fuzzy rules generation method, where the expert wants to classify the output variables in some fuzzy predefined classes.