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: In this paper, a class of recurrent neural networks (RNNs) with variable delays are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.
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: Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.
Abstract: The primary objective of this study is to test whether
there is any difference in performance between funded and nonfunded
registered charity organizations. In this study, performance as
the dependent variable is measured using total donations. Using a
sample of 101 charity organizations registered with the Registry of
Society, analysis of variance (ANOVA) results indicate that there is a
difference in financial performance between funded and non-funded
charity organizations. The study provides empirical evidence to
resource providers and the policy makers in scrutinizing the decision
to disburse their funds and resources to these charity organizations.
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: Views on therapists- attraction have influenced the ethical and professional development of the mental health fields. Because the majority of therapist attraction literature (63.6%) has been conducted from a psychoanalytic standpoint, approaches to attraction from feminist perspectives have not been adequately developed. Considering the lack of a feminist voice regarding attraction, this article attempts to offer a feminist perspective on this issue. The purpose of this article is to offer a feminist perspective on the phenomenon of attraction in order to raise awareness about the importance of power inequalities, intersectionalities, contextual variables and the need for action in the field.
Abstract: The authors present an algorithm for order reduction of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.
Abstract: While import-substituting industrialization policy
constitute the basis for the industrialization strategies of the 1960s
and 1970s in Turkey, this policy was no longer sustainable by the
1980s. For this reason, export-oriented industrialization policy was
adopted with the decisions taken on January 24, 1980. In other words,
the post-1980 period, Turkey's economy has adopted outwardoriented
industrialization strategy.
In this study, it is aimed to analyze the effect of the change in
economic structure on foreign trade with the transformation of
foreign trade and industrialization policies in the post-1980 period. In
this respect, in order to analyze the relationship between import,
export and economic growth by using variables of the 1960-2011
period, Chow test was applied. In the analysis the reason for using
Chow test is whether there is any difference in economic terms
between import-substituting industrialization policy applied in the
1960-1980 period and the 1981-2011 period during which exportoriented
industrialization policy was applied as a result of the
structural transformation.
Abstract: In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.
Abstract: Bendability is constrained by maximum top roller
load imparting capacity of the machine. Maximum load is
encountered during the edge pre-bending stage of roller bending.
Capacity of 3-roller plate bending machine is specified by
maximum thickness and minimum shell diameter combinations that
can be pre-bend for given plate material of maximum width.
Commercially available plate width or width of the plate that can be
accommodated on machine decides the maximum rolling width.
Original equipment manufacturers (OEM) provide the machine
capacity chart based on reference material considering perfectly
plastic material model. Reported work shows the bendability analysis
of heavy duty 3-roller plate bending machine. The input variables for
the industry are plate thickness, shell diameter and material property
parameters, as it is fixed by the design. Analytical models of
equivalent thickness, equivalent width and maximum width based on
power law material model were derived to study the bendability.
Equation of maximum width provides bendability for designed
configuration i.e. material property, shell diameter and thickness
combinations within the machine limitations. Equivalent thicknesses
based on perfectly plastic and power law material model were
compared for four different materials grades of C-Mn steel in order
to predict the bend-ability. Effect of top roller offset on the
bendability at maximum top roller load imparting capacity is
reported.
Abstract: In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.
Abstract: Using Turkish data, in this study it is investigated that
whether a firm’s ownership structure has an impact on its stock
prices after the crisis. A linear regression model is conducted on the
data of non-financial firms that are trading in Istanbul Stock
Exchange 100 Index (ISE 100) index. The findings show that, all
explanatory variables such as inside ownership, largest ownership,
concentrated ownership, foreign shareholders, family controlled and
dispersed ownership are not very important to explain stock prices
after the crisis. Family controlled firms and concentrated ownership
is positively related to stock price, dispersed ownership, largest
ownership, foreign shareholders, and inside ownership structures
have negative interaction between stock prices, but because of the p
value is not under the value of 0.05 this relation is not significant. In
addition, the analysis shows that, the shares of firms that have inside,
largest and dispersed ownership structure are outperform comparing
with the other firms. Furthermore, ownership concentrated firms
outperform to family controlled firms.
Abstract: The main objective of this study is to test the
relationship between numbers of variables representing the firm
characteristics (market-related variables) and the extent of voluntary
disclosure levels (forward-looking disclosure) in the annual reports of
Egyptian firms listed on the Egyptian Stock Exchange. The results
show that audit firm size is significantly positively correlated (in all
the three years) with the level of forward-looking disclosure.
However, industry type variable (which divided to: industries,
cement, construction, petrochemicals and services), is found being
insignificantly association with the level of forward-looking
information disclosed in the annual reports for all the three years.
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.
Abstract: In the context of computer numerical control (CNC) and computer aided manufacturing (CAM), the capabilities of programming languages such as symbolic and intuitive programming, program portability and geometrical portfolio have special importance. They allow to save time and to avoid errors during part programming and permit code re-usage. Our updated literature review indicates that the current state of art presents voids in parametric programming, program portability and programming flexibility. In response to this situation, this article presents a compiler implementation for EGCL (Extended G-code Language), a new, enriched CNC programming language which allows the use of descriptive variable names, geometrical functions and flow-control statements (if-then-else, while). Our compiler produces low-level generic, elementary ISO-compliant Gcode, thus allowing for flexibility in the choice of the executing CNC machine and in portability. Our results show that readable variable names and flow control statements allow a simplified and intuitive part programming and permit re-usage of the programs. Future work includes allowing the programmer to define own functions in terms of EGCL, in contrast to the current status of having them as library built-in functions.
Abstract: Optimization is often a critical issue for most system
design problems. Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, finding optimal solution to complex high
dimensional, multimodal problems often require highly
computationally expensive function evaluations and hence are
practically prohibitive. The Dynamic Approximate Fitness based
Hybrid EA (DAFHEA) model presented in our earlier work [14]
reduced computation time by controlled use of meta-models to
partially replace the actual function evaluation by approximate
function evaluation. However, the underlying assumption in
DAFHEA is that the training samples for the meta-model are
generated from a single uniform model. Situations like model
formation involving variable input dimensions and noisy data
certainly can not be covered by this assumption. In this paper we
present an enhanced version of DAFHEA that incorporates a
multiple-model based learning approach for the SVM approximator.
DAFHEA-II (the enhanced version of the DAFHEA framework) also
overcomes the high computational expense involved with additional
clustering requirements of the original DAFHEA framework. The
proposed framework has been tested on several benchmark functions
and the empirical results illustrate the advantages of the proposed
technique.
Abstract: In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.
Abstract: The paper presents the virtual model of the active
suspension system used for improving the dynamic behavior of a
motor vehicle. The study is focused on the design of the control
system, the purpose being to minimize the effect of the road
disturbances (which are considered as perturbations for the control
system). The analysis is performed for a quarter-car model, which
corresponds to the suspension system of the front wheel, by using the
DFC (Design for Control) software solution EASY5 (Engineering
Analysis Systems) of MSC Software. The controller, which is a PIDbased
device, is designed through a parametric optimization with the
Matrix Algebra Tool (MAT), considering the gain factors as design
variables, while the design objective is to minimize the overshoot of
the indicial response.
Abstract: The aim of this paper is to investigate the effect of
mean size of industry on survival of new firms in East-Azarbaijan
province through 1981-2006 using hazard function. So the effect of
two variables including mean employment of industry and mean
capital of industry are investigated on firm's survival. The Industry &
Mine Ministry database has used for data gathering and the data are
analyzed using the semi-parametric cox regression model. The results
of this study shows that there is a meaningful negative relationship
between mean capital of industry and firm's survival, but the mean
employment of industry has no meaningful effect on survival of new
firms.