Abstract: In this research a mathematical model for direct
oxidization of hydrogen sulfide into elemental sulfur in a fluidized
bed reactor with external circulation was developed. As the catalyst
is deactivated in the fluidized bed, it might be placed in a reduction
tank in order to remove sulfur through heating above its dew point.
The reactor model demonstrated via MATLAB software. It was
shown that variations of H2S conversion as well as; products formed
were reasonable in comparison with corresponding results of a fixed
bed reactor. Through analyzing results of this model, it became
possible to propose the main optimized operating conditions for the
process considered. These conditions included; the temperature range
of 100-130ºC and utilizing the catalyst as much as possible providing
the highest bed density respect to dimensions of bed, economical
aspects that the bed ever remained in fluidized mode. A high active
and stable catalyst under the optimum conditions exhibited 100%
conversion in a fluidized bed reactor.
Abstract: The gases generated in oil filled transformers can be
used for qualitative determination of incipient faults. The Dissolved
Gas Analysis has been widely used by utilities throughout the world
as the primarily diagnostic tool for transformer maintenance. In this
paper, various Artificial Intelligence Techniques that have been used
by the researchers in the past have been reviewed, some conclusions
have been drawn and a sequential hybrid system has been proposed.
The synergy of ANN and FIS can be a good solution for reliable
results for predicting faults because one should not rely on a single
technology when dealing with real–life applications.
Abstract: Overall cost is a significant consideration in any
decision-making process. Although many studies were carried out on
overall cost in construction, little has treated the uncertainties of real
life cycle development. On the basis of several case studies, a
feedback process was performed on the historical data of studied
buildings. This process enabled to identify some factors causing
uncertainty during the operational period. As a result, the research
proposes a new method for assessing the overall cost during a part of
the building-s life cycle taking account of the building actual value,
its end-of-life value and the influence of the identified life cycle
uncertainty factors. The findings are a step towards a higher level of
reliability in overall cost evaluation taking account of some usually
unexpected uncertainty factors.
Abstract: Wireless sensor networks are consisted of hundreds or
thousands of small sensors that have limited resources.
Energy-efficient techniques are the main issue of wireless sensor
networks. This paper proposes an energy efficient agent-based
framework in wireless sensor networks. We adopt biologically
inspired approaches for wireless sensor networks. Agent operates
automatically with their behavior policies as a gene. Agent aggregates
other agents to reduce communication and gives high priority to nodes
that have enough energy to communicate. Agent behavior policies are
optimized by genetic operation at the base station. Simulation results
show that our proposed framework increases the lifetime of each node.
Each agent selects a next-hop node with neighbor information and
behavior policies. Our proposed framework provides self-healing,
self-configuration, self-optimization properties to sensor nodes.
Abstract: The construction of a civil structure inside a urban
area inevitably modifies the outdoor microclimate at the building
site. Wind speed, wind direction, air pollution, driving rain, radiation
and daylight are some of the main physical aspects that are subjected
to the major changes. The quantitative amount of these modifications
depends on the shape, size and orientation of the building and on its
interaction with the surrounding environment.The flow field over a
flat roof model building has been numerically investigated in order to
determine two-dimensional CFD guidelines for the calculation of the
turbulent flow over a structure immersed in an atmospheric boundary
layer. To this purpose, a complete validation campaign has been
performed through a systematic comparison of numerical simulations
with wind tunnel experimental data.Several turbulence models and
spatial node distributions have been tested for five different vertical
positions, respectively from the upstream leading edge to the
downstream bottom edge of the analyzed model. Flow field
characteristics in the neighborhood of the building model have been
numerically investigated, allowing a quantification of the capabilities
of the CFD code to predict the flow separation and the extension of
the recirculation regions.The proposed calculations have allowed the
development of a preliminary procedure to be used as a guidance in
selecting the appropriate grid configuration and corresponding
turbulence model for the prediction of the flow field over a twodimensional
roof architecture dominated by flow separation.
Abstract: This paper proposed high level feature for online Lao handwritten recognition. This feature must be high level enough so that the feature is not change when characters are written by different persons at different speed and different proportion (shorter or longer stroke, head, tail, loop, curve). In this high level feature, a character is divided in to sequence of curve segments where a segment start where curve reverse rotation (counter clockwise and clockwise). In each segment, following features are gathered cumulative change in direction of curve (- for clockwise), cumulative curve length, cumulative length of left to right, right to left, top to bottom and bottom to top ( cumulative change in X and Y axis of segment). This feature is simple yet robust for high accuracy recognition. The feature can be gather from parsing the original time sampling sequence X, Y point of the pen location without re-sampling. We also experiment on other segmentation point such as the maximum curvature point which was widely used by other researcher. Experiments results show that the recognition rates are at 94.62% in comparing to using maximum curvature point 75.07%. This is due to a lot of variations of turning points in handwritten.
Abstract: In this paper, we show that the association of the PI
regulators for the speed and stator currents with a control strategy
using the linearization by state feedback for an induction machine
without speed sensor, and with an adaptation of the rotor resistance.
The rotor speed is estimated by using the model reference adaptive
system approach (MRAS). This method consists of using two
models: The first is the reference model and the second is an
adjustable one in which two components of the stator flux, obtained
from the measurement of the currents and stator voltages are
estimated. The estimated rotor speed is then obtained by canceling
the difference between stator-flux of the reference model and those
of the adjustable one. Satisfactory results of simulation are obtained
and discussed in this paper to highlight the proposed approach.
Abstract: This work is to study a roll of the fluctuating density
gradient in the compressible flows for the computational fluid dynamics
(CFD). A new anisotropy tensor with the fluctuating density
gradient is introduced, and is used for an invariant modeling technique
to model the turbulent density gradient correlation equation derived
from the continuity equation. The modeling equation is decomposed
into three groups: group proportional to the mean velocity, and that
proportional to the mean strain rate, and that proportional to the mean
density. The characteristics of the correlation in a wake are extracted
from the results by the two dimensional direct simulation, and shows
the strong correlation with the vorticity in the wake near the body.
Thus, it can be concluded that the correlation of the density gradient
is a significant parameter to describe the quick generation of the
turbulent property in the compressible flows.
Abstract: In this paper, we apply a semismooth active set method to image inpainting. The method exploits primal and dual features of a proposed regularized total variation model, following after the technique presented in [4]. Numerical results show that the method is fast and efficient in inpainting sufficiently thin domains.
Abstract: Modeling of the distributed systems allows us to
represent the whole its functionality. The working system instance
rarely fulfils the whole functionality represented by model; usually
some parts of this functionality should be accessible periodically.
The reporting system based on the Data Warehouse concept seams to
be an intuitive example of the system that some of its functionality is
required only from time to time. Analyzing an enterprise risk
associated with the periodical change of the system functionality, we
should consider not only the inaccessibility of the components
(object) but also their functions (methods), and the impact of such a
situation on the system functionality from the business point of view.
In the paper we suggest that the risk attributes should be estimated
from risk attributes specified at the requirements level (Use Case in
the UML model) on the base of the information about the structure of
the model (presented at other levels of the UML model). We argue
that it is desirable to consider the influence of periodical changes in
requirements on the enterprise risk estimation. Finally, the
proposition of such a solution basing on the UML system model is
presented.
Abstract: In this article, a mathematical programming model
for choosing an optimum portfolio of investments is developed.
The investments are considered as investment projects. The
uncertainties of the real world are associated through fuzzy
concepts for coefficients of the proposed model (i. e. initial
investment costs, profits, resource requirement, and total available
budget). Model has been coded by using LINGO 11.0 solver. The
results of a full analysis of optimistic and pessimistic derivative
models are promising for selecting an optimum portfolio of
projects in presence of uncertainty.
Abstract: In this paper, stabilization of an Active Magnetic Bearing (AMB) system with varying rotor speed using Sliding Mode Control (SMC) technique is considered. The gyroscopic effect inherited in the system is proportional to rotor speed in which this nonlinearity effect causes high system instability as the rotor speed increases. Also, transformation of the AMB dynamic model into a new class of uncertain system shows that this gyroscopic effect lies in the mismatched part of the system matrix. Moreover, the current gain parameter is allowed to be varied in a known bound as an uncertainty in the input matrix. SMC design method is proposed in which the sufficient condition that guarantees the global exponential stability of the reduced-order system is represented in Linear Matrix Inequality (LMI). Then, a new chattering-free control law is established such that the system states are driven to reach the switching surface and stay on it thereafter. The performance of the controller applied to the AMB model is demonstrated through simulation works under various system conditions.
Abstract: Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.
Abstract: Fuzzy C-means Clustering algorithm (FCM) is a
method that is frequently used in pattern recognition. It has the
advantage of giving good modeling results in many cases, although,
it is not capable of specifying the number of clusters by itself. In
FCM algorithm most researchers fix weighting exponent (m) to a
conventional value of 2 which might not be the appropriate for all
applications. Consequently, the main objective of this paper is to use
the subtractive clustering algorithm to provide the optimal number of
clusters needed by FCM algorithm by optimizing the parameters of
the subtractive clustering algorithm by an iterative search approach
and then to find an optimal weighting exponent (m) for the FCM
algorithm. In order to get an optimal number of clusters, the iterative
search approach is used to find the optimal single-output Sugenotype
Fuzzy Inference System (FIS) model by optimizing the
parameters of the subtractive clustering algorithm that give minimum
least square error between the actual data and the Sugeno fuzzy
model. Once the number of clusters is optimized, then two
approaches are proposed to optimize the weighting exponent (m) in
the FCM algorithm, namely, the iterative search approach and the
genetic algorithms. The above mentioned approach is tested on the
generated data from the original function and optimal fuzzy models
are obtained with minimum error between the real data and the
obtained fuzzy models.
Abstract: In this paper, the effects of the restoring force device on the response of a space frame structure resting on sliding type of bearing with a restoring force device is studied. The NS component of the El - Centro earthquake and harmonic ground acceleration is considered for earthquake excitation. The structure is modeled by considering six-degrees of freedom (three translations and three rotations) at each node. The sliding support is modeled as a fictitious spring with two horizontal degrees of freedom. The response quantities considered for the study are the top floor acceleration, base shear, bending moment and base displacement. It is concluded from the study that the displacement of the structure reduces by the use of the restoring force device. Also, the peak values of acceleration, bending moment and base shear also decreases. The simulation results show the effectiveness of the developed and proposed method.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Abstract: Wireless sensor networks have been used in wide
areas of application and become an attractive area for researchers in
recent years. Because of the limited energy storage capability of
sensor nodes, Energy consumption is one of the most challenging
aspects of these networks and different strategies and protocols deals
with this area. This paper presents general methods for designing low
power wireless sensor network. Different sources of energy
consumptions in these networks are discussed here and techniques for
alleviating the consumption of energy are presented.
Abstract: Artificial Immune System is applied as a Heuristic
Algorithm for decades. Nevertheless, many of these applications
took advantage of the benefit of this algorithm but seldom proposed
approaches for enhancing the efficiency. In this paper, a
Self-evolving Artificial Immune System is proposed via developing
the T and B cell in Immune System and built a self-evolving
mechanism for the complexities of different problems. In this
research, it focuses on enhancing the efficiency of Clonal selection
which is responsible for producing Affinities to resist the invading of
Antigens. T and B cell are the main mechanisms for Clonal
Selection to produce different combinations of Antibodies.
Therefore, the development of T and B cell will influence the
efficiency of Clonal Selection for searching better solution.
Furthermore, for better cooperation of the two cells, a co-evolutional
strategy is applied to coordinate for more effective productions of
Antibodies. This work finally adopts Flow-shop scheduling
instances in OR-library to validate the proposed algorithm.
Abstract: Network security attacks are the violation of
information security policy that received much attention to the
computational intelligence society in the last decades. Data mining
has become a very useful technique for detecting network intrusions
by extracting useful knowledge from large number of network data
or logs. Naïve Bayesian classifier is one of the most popular data
mining algorithm for classification, which provides an optimal way
to predict the class of an unknown example. It has been tested that
one set of probability derived from data is not good enough to have
good classification rate. In this paper, we proposed a new learning
algorithm for mining network logs to detect network intrusions
through naïve Bayesian classifier, which first clusters the network
logs into several groups based on similarity of logs, and then
calculates the prior and conditional probabilities for each group of
logs. For classifying a new log, the algorithm checks in which cluster
the log belongs and then use that cluster-s probability set to classify
the new log. We tested the performance of our proposed algorithm by
employing KDD99 benchmark network intrusion detection dataset,
and the experimental results proved that it improves detection rates
as well as reduces false positives for different types of network
intrusions.
Abstract: Study of fire and explosion is very important mainly
in oil and gas industries due to several accidents which have been
reported in the past and present. In this work, we have investigated
the flammability of bio oil vapour mixtures. This mixture may
contribute to fire during the storage and transportation process. Bio
oil sample derived from Palm Kernell shell was analysed using Gas
Chromatography Mass Spectrometry (GC-MS) to examine the
composition of the sample. Mole fractions of 12 selected
components in the liquid phase were obtained from the GC-FID data
and used to calculate mole fractions of components in the gas phase
via modified Raoult-s law. Lower Flammability Limits (LFLs) and
Upper Flammability Limits (UFLs) for individual components were
obtained from published literature. However, stoichiometric
concentration method was used to calculate the flammability limits
of some components which their flammability limit values are not
available in the literature. The LFL and UFL values for the mixture
were calculated using the Le Chatelier equation. The LFLmix and
UFLmix values were used to construct a flammability diagram and
subsequently used to determine the flammability of the mixture. The
findings of this study can be used to propose suitable inherently
safer method to prevent the flammable mixture from occurring and
to minimizing the loss of properties, business, and life due to fire
accidents in bio oil productions.