Abstract: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: Many aluminum motorcycle parts produced by a high
pressure die casting. Some parts such as fuel caps were a thin and
complex shape. This part risked for porosities and blisters on surface
if it only depended on an experience of mold makers for mold design.
This research attempted to use CAST-DESIGNER software
simulated the high pressure die casting process with the same process
parameters of a motorcycle fuel cap production. The simulated results
were compared with fuel cap products and expressed the same
porosity and blister locations on cap surface. An average of absolute
difference of simulated results was obtained 0.094 mm when
compared the simulated porosity and blister defect sizes on the fuel
cap surfaces with the experimental micro photography. This
comparison confirmed an accuracy of software and will use the
setting parameters to improve fuel cap molds in the further work.
Abstract: This paper introduces an original method of
parametric optimization of the structure for multimodal decisionlevel
fusion scheme which combines the results of the partial solution
of the classification task obtained from assembly of the mono-modal
classifiers. As a result, a multimodal fusion classifier which has the
minimum value of the total error rate has been obtained.
Abstract: The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
Abstract: Estimation of model parameters is necessary to predict
the behavior of a system. Model parameters are estimated using
optimization criteria. Most algorithms use historical data to estimate
model parameters. The known target values (actual) and the output
produced by the model are compared. The differences between the
two form the basis to estimate the parameters. In order to compare
different models developed using the same data different criteria are
used. The data obtained for short scale projects are used here. We
consider software effort estimation problem using radial basis
function network. The accuracy comparison is made using various
existing criteria for one and two predictors. Then, we propose a new
criterion based on linear least squares for evaluation and compared
the results of one and two predictors. We have considered another
data set and evaluated prediction accuracy using the new criterion.
The new criterion is easy to comprehend compared to single statistic.
Although software effort estimation is considered, this method is
applicable for any modeling and prediction.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Urban areas have been expanded throughout the
globe. Monitoring and modelling urban growth have become a
necessity for a sustainable urban planning and decision making.
Urban prediction models are important tools for analyzing the causes
and consequences of urban land use dynamics. The objective of this
research paper is to analyze and model the urban change, which has
been occurred from 1990 to 2000 using CORINE land cover maps.
The model was developed using drivers of urban changes (such as
road distance, slope, etc.) under an Artificial Neural Network
modelling approach. Validation was achieved using a prediction map
for 2006 which was compared with a real map of Urban Atlas of
2006. The accuracy produced a Kappa index of agreement of 0,639
and a value of Cramer's V of 0,648. These encouraging results
indicate the importance of the developed urban growth prediction
model which using a set of available common biophysical drivers
could serve as a management tool for the assessment of urban
change.
Abstract: Modelling of the earth's surface and evaluation of
urban environment, with 3D models, is an important research topic.
New stereo capabilities of high resolution optical satellites images,
such as the tri-stereo mode of Pleiades, combined with new image
matching algorithms, are now available and can be applied in urban
area analysis. In addition, photogrammetry software packages gained
new, more efficient matching algorithms, such as SGM, as well as
improved filters to deal with shadow areas, can achieve more dense
and more precise results.
This paper describes a comparison between 3D data extracted
from tri-stereo and dual stereo satellite images, combined with pixel
based matching and Wallis filter. The aim was to improve the
accuracy of 3D models especially in urban areas, in order to assess if
satellite images are appropriate for a rapid evaluation of urban
environments.
The results showed that 3D models achieved by Pleiades tri-stereo
outperformed, both in terms of accuracy and detail, the result
obtained from a Geo-eye pair. The assessment was made with
reference digital surface models derived from high resolution aerial
photography. This could mean that tri-stereo images can be
successfully used for the proposed urban change analyses.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
Abstract: A novel hybrid model of the lumbar spine, allowing
fast static and dynamic simulations of the disc pressure
and the spine mobility, is introduced in this work. Our
contribution is to combine rigid bodies, deformable finite
elements, articular constraints, and springs into a unique model
of the spine. Each vertebra is represented by a rigid body
controlling a surface mesh to model contacts on the facet
joints and the spinous process. The discs are modeled using
a heterogeneous tetrahedral finite element model. The facet
joints are represented as elastic joints with six degrees of
freedom, while the ligaments are modeled using non-linear
one-dimensional elastic elements. The challenge we tackle
is to make these different models efficiently interact while
respecting the principles of Anatomy and Mechanics.
The mobility, the intradiscal pressure, the facet joint force and
the instantaneous center of rotation of the lumbar spine are
validated against the experimental and theoretical results of
the literature on flexion, extension, lateral bending as well as
axial rotation.
Our hybrid model greatly simplifies the modeling task and
dramatically accelerates the simulation of pressure within the
discs, as well as the evaluation of the range of motion and the
instantaneous centers of rotation, without penalizing precision.
These results suggest that for some types of biomechanical
simulations, simplified models allow far easier modeling and
faster simulations compared to usual full-FEM approaches
without any loss of accuracy.
Abstract: This paper presents modeling of an Alternating
Current (AC) Photovoltaic (PV) module using Matlab/Simulink. The
proposed AC-PV module model is simple, realistic, and application
oriented. The model is derived on module level as compared to cell
level directly from the information provided by the manufacturer data
sheet. DC-PV module, MPPT control, BC, VSI and LC filter, all
were treated as a single unit. The model accounts for changes in
variations of both irradiance and temperature. The AC-PV module
proposed model is simulated and the results are compared with the
datasheet projected numbers to validate model’s accuracy and
effectiveness. Implementation and results demonstrate simplicity and
accuracy, as well as reliability of the model.
Abstract: In this paper, a new trend for improvement in semianalytical
method based on scale boundaries in order to solve the 2D
elastodynamic problems is provided. In this regard, only the
boundaries of the problem domain discretization are by specific subparametric
elements. Mapping functions are uses as a class of higherorder
Lagrange polynomials, special shape functions, Gauss-Lobatto-
Legendre numerical integration, and the integral form of the weighted
residual method, the matrix is diagonal coefficients in the equations
of elastodynamic issues. Differences between study conducted and
prior research in this paper is in geometry production procedure of
the interpolation function and integration of the different is selected.
Validity and accuracy of the present method are fully demonstrated
through two benchmark problems which are successfully modeled
using a few numbers of DOFs. The numerical results agree very well
with the analytical solutions and the results from other numerical
methods.
Abstract: Iris codes contain bits with different entropy. This
work investigates different strategies to reduce the size of iris
code templates with the aim of reducing storage requirements and
computational demand in the matching process. Besides simple subsampling
schemes, also a binary multi-resolution representation as
used in the JBIG hierarchical coding mode is assessed. We find that
iris code template size can be reduced significantly while maintaining
recognition accuracy. Besides, we propose a two-stage identification
approach, using small-sized iris code templates in a pre-selection
stage, and full resolution templates for final identification, which
shows promising recognition behaviour.
Abstract: Fracture in hot precision forging of engine valves was
investigated in this paper. The entire valve forging procedure was
described and the possible cause of the fracture was proposed. Finite
Element simulation was conducted for the forging process, with
commercial Finite Element code DEFORMTM. The effects of
material properties, the effect of strain rate and temperature were
considered in the FE simulation. Two fracture criteria were discussed
and compared, based on the accuracy and reliability of the FE
simulation results. The selected criterion predicted the fracture
location and shows the trend of damage increasing with good
accuracy, which matches the experimental observation. Additional
modification of the punch shapes was proposed to further reduce the
tendency of fracture in forging. Finite Element comparison shows a
great potential of such application in the mass production.
Abstract: Digital image correlation (DIC) is a contactless fullfield
displacement and strain reconstruction technique commonly
used in the field of experimental mechanics. Comparing with
physical measuring devices, such as strain gauges, which only
provide very restricted coverage and are expensive to deploy widely,
the DIC technique provides the result with full-field coverage and
relative high accuracy using an inexpensive and simple experimental
setup. It is very important to study the natural patterns effect on the
DIC technique because the preparation of the artificial patterns is
time consuming and hectic process. The objective of this research is
to study the effect of using images having natural pattern on the
performance of DIC. A systematical simulation method is used to
build simulated deformed images used in DIC. A parameter (subset
size) used in DIC can have an effect on the processing and accuracy
of DIC and even cause DIC to failure. Regarding to the picture
parameters (correlation coefficient), the higher similarity of two
subset can lead the DIC process to fail and make the result more
inaccurate. The pictures with good and bad quality for DIC methods
have been presented and more importantly, it is a systematic way to
evaluate the quality of the picture with natural patterns before they
install the measurement devices.
Abstract: Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: Examining existing experimental results for shallow
rigid foundations subjected to vertical centric load (N), accompanied
or not with a bending moment (M), two main non-linear mechanisms
governing the cyclic response of the soil-foundation system can be
distinguished: foundation uplift and soil yielding. A soil-foundation
failure limit, is defined as a domain of resistance in the two
dimensional (2D) load space (N, M) inside of which lie all the
admissible combinations of loads; these latter correspond to a pure
elastic, non-linear elastic or plastic behavior of the soil-foundation
system, while the points lying on the failure limit correspond to a
combination of loads leading to a failure of the soil-foundation
system. In this study, the proposed resistance domain is constructed
analytically based on mechanics. Original elastic limit, uplift
initiation limit and iso-uplift limits are constructed inside this
domain. These limits give a prediction of the mechanisms activated
for each combination of loads applied to the foundation. A
comparison of the proposed failure limit with experimental tests
existing in the literature shows interesting results. Also, the
developed uplift initiation limit and iso-uplift curves are confronted
with others already proposed in the literature and widely used due to
the absence of other alternatives, and remarkable differences are
noted, showing evident errors in the past proposals and relevant
accuracy for those given in the present work.