Abstract: Web usage mining algorithms have been widely
utilized for modeling user web navigation behavior. In this study we
advance a model for mining of user-s navigation pattern. The model
makes user model based on expectation-maximization (EM)
algorithm.An EM algorithm is used in statistics for finding maximum
likelihood estimates of parameters in probabilistic models, where the
model depends on unobserved latent variables. The experimental
results represent that by decreasing the number of clusters, the log
likelihood converges toward lower values and probability of the
largest cluster will be decreased while the number of the clusters
increases in each treatment.
Abstract: This paper proposes a new decision making approch
based on quantitative possibilistic influence diagrams which are
extension of standard influence diagrams in the possibilistic framework.
We will in particular treat the case where several expert
opinions relative to value nodes are available. An initial expert assigns
confidence degrees to other experts and fixes a similarity threshold
that provided possibility distributions should respect. To illustrate our
approach an evaluation algorithm for these multi-source possibilistic
influence diagrams will also be proposed.
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: Modeling of Panel Zone (PZ) seismic behavior,
because of its role in overall ductility and lateral stiffness of steel
moment frames, has been considered a challenge for years. There are
some studies regarding the effects of different doubler plates
thicknesses and geometric properties of PZ on its seismic behavior.
However, there is not much investigation on the effects of number of
provided continuity plates in case of presence of one triangular
haunch, two triangular haunches and rectangular haunch (T shape
haunches) for exterior columns. In this research first detailed finite
element models of 12tested connection of SAC joint venture were
created and analyzed then obtained cyclic behavior backbone curves
of these models besides other FE models for similar tests were used
for neural network training. Then seismic behavior of these data is
categorized according to continuity plate-s arrangements and
differences in type of haunches. PZ with one-sided haunches have
little plastic rotation. As the number of continuity plates increases
due to presence of two triangular haunches (four continuity plate),
there will be no plastic rotation, in other words PZ behaves in its
elastic range. In the case of rectangular haunch, PZ show more plastic
rotation in comparison with one-sided triangular haunch and
especially double-sided triangular haunches. Moreover, the models
that will be presented in case of triangular one-sided and double-
sided haunches and rectangular haunches as a result of this study
seem to have a proper estimation of PZ seismic behavior.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.
Abstract: This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.
Abstract: Saturated hydraulic conductivity of Soil is an
important property in processes involving water and solute flow in
soils. Saturated hydraulic conductivity of soil is difficult to measure
and can be highly variable, requiring a large number of replicate
samples. In this study, 60 sets of soil samples were collected at
Saqhez region of Kurdistan province-IRAN. The statistics such as
Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean
Bias Error (MBE) and Mean Absolute Error (MAE) were used to
evaluation the multiple linear regression models varied with number
of dataset. In this study the multiple linear regression models were
evaluated when only percentage of sand, silt, and clay content (SSC)
were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd)
were used as inputs. The R, RMSE, MBE and MAE values of the 50
dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and
12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11
and 12.92, respectively, for relationship obtained from multiple
linear regressions on data. Also the R, RMSE, MBE and MAE values
of the 10 dataset for method (SSC), were calculated 0.725, 19.62, -
9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618,
24.69, -17.37 and 22.16, respectively, which shows when number of
dataset increase, precision of estimated saturated hydraulic
conductivity, increases.
Abstract: The triumph of inductive neuro-stimulation since its rediscovery in the 1980s has been quite spectacular. In lots of branches ranging from clinical applications to basic research this system is absolutely indispensable. Nevertheless, the basic knowledge about the processes underlying the stimulation effect is still very rough and rarely refined in a quantitative way. This seems to be not only an inexcusable blank spot in biophysics and for stimulation prediction, but also a fundamental hindrance for technological progress. The already very sophisticated devices have reached a stage where further optimization requires better strategies than provided by simple linear membrane models of integrate-and-fire style. Addressing this problem for the first time, we suggest in the following text a way for virtual quantitative analysis of a stimulation system. Concomitantly, this ansatz seems to provide a route towards a better understanding by using nonlinear signal processing and taking the nerve as a filter that is adapted for neuronal magnetic stimulation. The model is compact and easy to adjust. The whole setup behaved very robustly during all performed tests. Exemplarily a recent innovative stimulator design known as cTMS is analyzed and dimensioned with this approach in the following. The results show hitherto unforeseen potentials.
Abstract: Computer programming is considered a very difficult
course by many computer science students. The reasons for the
difficulties include cognitive load involved in programming,
different learning styles of students, instructional methodology and
the choice of the programming languages. To reduce the difficulties
the following have been tried: pair programming, program
visualization, different learning styles etc. However, these efforts
have produced limited success. This paper reviews the problem and
proposes a framework to help students overcome the difficulties
involved.
Abstract: This paper develops driver reaction-time models for
car-following analysis based on human factors. The reaction time
was classified as brake-reaction time (BRT) and
acceleration/deceleration reaction time (ADRT). The BRT occurs
when the lead vehicle is barking and its brake light is on, while the
ADRT occurs when the driver reacts to adjust his/her speed using the
gas pedal only. The study evaluates the effect of driver
characteristics and traffic kinematic conditions on the driver reaction
time in a car-following environment. The kinematic conditions
introduced urgency and expectancy based on the braking behaviour
of the lead vehicle at different speeds and spacing. The kinematic
conditions were used for evaluating the BRT and are classified as
normal, surprised, and stationary. Data were collected on a driving
simulator integrated into a real car and included the BRT and ADRT
(as dependent variables) and driver-s age, gender, driving experience,
driving intensity (driving hours per week), vehicle speed, and
spacing (as independent variables). The results showed that there was
a significant difference in the BRT at normal, surprised, and
stationary scenarios and supported the hypothesis that both urgency
and expectancy had significant effects on BRT. Driver-s age, gender,
speed, and spacing were found to be significant variables for the
BRT in all scenarios. The results also showed that driver-s age and
gender were significant variables for the ADRT. The research
presented in this paper is part of a larger project to develop a driversensitive
in-vehicle rear-end collision warning system.
Abstract: Rapid enlargement and physical development of cities
have facilitated the emergence of a number of city life crises and
decrease of environment quality. Subsequently, the need for noticing
the concept of quality and its improvement in urban environments,
besides quantitative issues, is obviously recognized. In the domain of
urban ideas the importance of taking these issues into consideration
is obvious not only in accordance to sustainable development
concepts and improvement of public environment quality, but also in
the enhancement of social and behavioral models.
The major concern of present article is to study the nature of urban
environment quality in urban development plans, which is important
not only in the concept and the aim of projects but also in their
execution procedure. As a result, this paper is going to utilize
planning capacities caused by environmental virtues in the planning
procedure of Moft Abad neighborhood. Thus, at the first step,
applying the Analytical Hierarchy Process (AHP), it has assessed
quantitative environmental issues. The present conditions of Moft
Abad state that “the neighborhood is generally suffering from the
lack of qualitative parameters, and the previously formed planning
procedures could not take the sustainable and developmental paths
which are aimed at environment quality virtues." The diminution of
economical and environmental virtues has resulted in the diminution
of residential and social virtues. Therefore, in order to enhance the
environment quality in Moft Abad, the present paper has tried to
supply the subject plans in order to make a safe, healthy, and lively
neighborhood.
Abstract: The zero inflated models are usually used in modeling
count data with excess zeros where the existence of the excess zeros
could be structural zeros or zeros which occur by chance. These type
of data are commonly found in various disciplines such as finance,
insurance, biomedical, econometrical, ecology, and health sciences
which involve sex and health dental epidemiology. The most popular
zero inflated models used by many researchers are zero inflated
Poisson and zero inflated negative binomial models. In addition, zero
inflated generalized Poisson and zero inflated double Poisson models
are also discussed and found in some literature. Recently zero
inflated inverse trinomial model and zero inflated strict arcsine
models are advocated and proven to serve as alternative models in
modeling overdispersed count data caused by excessive zeros and
unobserved heterogeneity. The purpose of this paper is to review
some related literature and provide a variety of examples from
different disciplines in the application of zero inflated models.
Different model selection methods used in model comparison are
discussed.
Abstract: This paper presents the review of past studies
concerning mathematical models for rescheduling passenger railway
services, as part of delay management in the occurrence of railway
disruption. Many past mathematical models highlighted were aimed
at minimizing the service delays experienced by passengers during
service disruptions. Integer programming (IP) and mixed-integer
programming (MIP) models are critically discussed, focusing on the
model approach, decision variables, sets and parameters. Some of
them have been tested on real-life data of railway companies
worldwide, while a few have been validated on fictive data. Based
on selected literatures on train rescheduling, this paper is able to
assist researchers in the model formulation by providing
comprehensive analyses towards the model building. These analyses
would be able to help in the development of new approaches in
rescheduling strategies or perhaps to enhance the existing
rescheduling models and make them more powerful or more
applicable with shorter computing time.
Abstract: In the planning point of view, it is essential to have
mode choice, due to the massive amount of incurred in transportation
systems. The intercity travellers in Libya have distinct features, as
against travellers from other countries, which includes cultural and
socioeconomic factors. Consequently, the goal of this study is to
recognize the behavior of intercity travel using disaggregate models,
for projecting the demand of nation-level intercity travel in Libya.
Multinomial Logit Model for all the intercity trips has been
formulated to examine the national-level intercity transportation in
Libya. The Multinomial logit model was calibrated using nationwide
revealed preferences (RP) and stated preferences (SP) survey. The
model was developed for deference purpose of intercity trips (work,
social and recreational). The variables of the model have been
predicted based on maximum likelihood method. The data needed for
model development were obtained from all major intercity corridors
in Libya. The final sample size consisted of 1300 interviews. About
two-thirds of these data were used for model calibration, and the
remaining parts were used for model validation. This study, which is
the first of its kind in Libya, investigates the intercity traveler’s
mode-choice behavior. The intercity travel mode-choice model was
successfully calibrated and validated. The outcomes indicate that, the
overall model is effective and yields higher precision of estimation.
The proposed model is beneficial, due to the fact that, it is receptive
to a lot of variables, and can be employed to determine the impact of
modifications in the numerous characteristics on the need for various
travel modes. Estimations of the model might also be of valuable to
planners, who can estimate possibilities for various modes and
determine the impact of unique policy modifications on the need for
intercity travel.
Abstract: Exploding concentrated underwater charges to
damage underwater structures such as ship hulls is a part of naval
warfare strategies. Adding small amounts of foreign particles (like
clay or silica) of nanosize significantly improves the engineering
properties of the polymers. In the present work the clay in terms 1, 2
and 3 percent by weight was surface treated with a suitable silane
agent. The hybrid nanocomposite was prepared by the hand lay-up
technique. Mathematical regression models have been employed for
theoretical prediction. This will result in considerable savings in terms of project time, effort and cost.
Abstract: Nonlinear finite element method with eight noded
isoparametric quadrilateral element is used for prediction of loaddeformation
behavior including bearing capacity of foundations.
Modified generalized plasticity model with non-associated flow rule
is applied for analysis of soil-footing system. Also Von Mises and
Tresca criterions are used for simulation of soil behavior. Modified
generalized plasticity model is able to simulate load-deformation
including softening behavior. Localization phenomena are considered
by different meshes. Localization phenomena have not been seen in
the examples. Predictions by modified generalized plasticity model
show good agreement with laboratory data and theoretical prediction
in comparison the other models.
Abstract: The radiative exchange method is introduced as a
numerical method for the simulation of radiative heat transfer in an
absorbing, emitting and isotropically scattering media. In this
method, the integro-differential radiative balance equation is solved
by using a new introduced concept for the exchange factor. Even
though the radiative source term is calculated in a mesh structure that
is coarser than the structure used in computational fluid dynamics,
calculating the exchange factor between different coarse elements by
using differential integration elements makes the result of the method
close to that of integro-differential radiative equation. A set of
equations for calculating exchange factors in two and threedimensional
Cartesian coordinate system is presented, and the
method is used in the simulation of radiative heat transfer in twodimensional
rectangular case and a three-dimensional simple cube.
The result of using this method in simulating different cases is
verified by comparing them with those of using other numerical
radiative models.
Abstract: A 7-step method (with 25 sub-steps) to assess risk of
air pollutants is introduced. These steps are: pre-considerations,
sampling, statistical analysis, exposure matrix and likelihood, doseresponse
matrix and likelihood, total risk evaluation, and discussion
of findings. All mentioned words and expressions are wellunderstood;
however, almost all steps have been modified, improved,
and coupled in such a way that a comprehensive method has been
prepared. Accordingly, the SADRA (Statistical Analysis-Driven Risk
Assessment) emphasizes extensive and ongoing application of
analytical statistics in traditional risk assessment models. A Sulfur
Dioxide case study validates the claim and provides a good
illustration for this method.
Abstract: A new paradigm for software design and development models software by its business process, translates the model into a process execution language, and has it run by a supporting execution engine. This process-oriented paradigm promotes modeling of software by less technical users or business analysts as well as rapid development. Since business process models may be shared by different organizations and sometimes even by different business domains, it is interesting to apply a technique used in traditional software component technology to design reusable business processes. This paper discusses an approach to apply a technique for software component fabrication to the design of process-oriented software units, called process components. These process components result from decomposing a business process of a particular application domain into subprocesses with an aim that the process components can be reusable in different process-based software models. The approach is quantitative because the quality of process component design is measured from technical features of the process components. The approach is also strategic because the measured quality is determined against business-oriented component management goals. A software tool has been developed to measure how good a process component design is, according to the required managerial goals and comparing to other designs. We also discuss how we benefit from reusable process components.
Abstract: Adsorption of Toluidine blue dye from aqueous solutions onto Neem Leaf Powder (NLP) has been investigated. The surface characterization of this natural material was examined by Particle size analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) spectroscopy and X-Ray Diffraction (XRD). The effects of process parameters such as initial concentration, pH, temperature and contact duration on the adsorption capacities have been evaluated, in which pH has been found to be most effective parameter among all. The data were analyzed using the Langmuir and Freundlich for explaining the equilibrium characteristics of adsorption. And kinetic models like pseudo first- order, second-order model and Elovich equation were utilized to describe the kinetic data. The experimental data were well fitted with Langmuir adsorption isotherm model and pseudo second order kinetic model. The thermodynamic parameters, such as Free energy of adsorption (AG"), enthalpy change (AH') and entropy change (AS°) were also determined and evaluated.