Abstract: Usually, the solid-fuel flow of an iron ore sinter plant
consists of different types of the solid-fuels, which differ from each
other. Information about the composition of the solid-fuel flow
usually comes every 8-24 hours. It can be clearly seen that this
information cannot be used to control the sintering process in real
time. Due to this, we propose an expert system which uses indirect
measurements from the process in order to obtain the composition of
the solid-fuel flow by solving an optimization task. Then this
information can be used to control the sintering process. The
proposed technique can be successfully used to improve sinter
quality and reduce the amount of solid-fuel used by the process.
Abstract: In this paper we compare the accuracy of data mining
methods to classifying students in order to predicting student-s class
grade. These predictions are more useful for identifying weak
students and assisting management to take remedial measures at early
stages to produce excellent graduate that will graduate at least with
second class upper. Firstly we examine single classifiers accuracy on
our data set and choose the best one and then ensembles it with a
weak classifier to produce simple voting method. We present results
show that combining different classifiers outperformed other single
classifiers for predicting student performance.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents and evolutionary
computation techniques in legacy e-learning environments. It
introduces the design and development of a scalable and
interoperable integration platform supporting:
I) various assessment agents for e-learning environments,
II) a specific resource retrieval agent for the provision of
additional information from Internet sources matching the
needs and profile of the specific user and
III) a genetic algorithm designed to extract efficient information
(classifying rules) based on the students- answering input
data.
The agents are implemented in order to provide intelligent
assessment services based on computational intelligence techniques
such as Bayesian Networks and Genetic Algorithms.
The proposed Genetic Algorithm (GA) is used in order to extract
efficient information (classifying rules) based on the students-
answering input data. The idea of using a GA in order to fulfil this
difficult task came from the fact that GAs have been widely used in
applications including classification of unknown data.
The utilization of new and emerging technologies like web
services allows integrating the provided services to any web based
legacy e-learning environment.
Abstract: Power system state estimation is the process of
calculating a reliable estimate of the power system state vector
composed of bus voltages' angles and magnitudes from telemetered
measurements on the system. This estimate of the state vector
provides the description of the system necessary for the operation
and security monitoring. Many methods are described in the
literature for solving the state estimation problem, the most important
of which are the classical weighted least squares method and the nondeterministic
genetic based method; however both showed
drawbacks. In this paper a modified version of the genetic
algorithm power system state estimation is introduced, Sensitivity of
the proposed algorithm to genetic operators is discussed, the
algorithm is applied to case studies and finally it is compared with
the classical weighted least squares method formulation.
Abstract: A dual-reciprocity boundary element method is presented
for the numerical solution of a class of axisymmetric elastodynamic
problems. The domain integrals that arise in the integrodifferential
formulation are converted to line integrals by using the
dual-reciprocity method together suitably constructed interpolating
functions. The second order time derivatives of the displacement
in the governing partial differential equations are suppressed by
using Laplace transformation. In the Laplace transform domain, the
problem under consideration is eventually reduced to solving a system
of linear algebraic equations. Once the linear algebraic equations are
solved, the displacement and stress fields in the physical domain can
be recovered by using a numerical technique for inverting Laplace
transforms.
Abstract: In this paper, we study on color transformation
method on website images for the color blind. The most common
category of color blindness is red-green color blindness which is
viewed as beige color. By transforming the colors of the images, the
color blind can improve their color visibility. They can have a better
view when browsing through the websites. To transform colors on
the website images, we study on two algorithms which are the
conversion techniques from RGB color space to HSV color space and
self-organizing color transformation. The comparative study focuses
on criteria based on the ease of use, quality, accuracy and efficiency.
The outcome of the study leads to enhancement of website images to
meet the color blinds- vision requirements in perceiving image
detailed.
Abstract: This study presents an active vibration control
technique to reduce the earthquake responses of a retained structural
system. The proposed technique is a synthesis of the adaptive input
estimation method (AIEM) and linear quadratic Gaussian (LQG)
controller. The AIEM can estimate an unknown system input online.
The LQG controller offers optimal control forces to suppress
wall-structural system vibration. The numerical results show robust
performance in the active vibration control technique.
Abstract: Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Multicarrier transmission system such as Orthogonal
Frequency Division Multiplexing (OFDM) is a promising technique
for high bit rate transmission in wireless communication system.
OFDM is a spectrally efficient modulation technique that can achieve
high speed data transmission over multipath fading channels without
the need for powerful equalization techniques. However the price
paid for this high spectral efficiency and less intensive equalization
is low power efficiency. OFDM signals are very sensitive to nonlinear
effects due to the high Peak-to-Average Power Ratio (PAPR),
which leads to the power inefficiency in the RF section of the
transmitter. This paper investigates the effect of PAPR reduction on
the performance parameter of multicarrier communication system.
Performance parameters considered are power consumption of Power
Amplifier (PA) and Digital-to-Analog Converter (DAC), power amplifier
efficiency, SNR of DAC and BER performance of the system.
From our analysis it is found that irrespective of PAPR reduction
technique being employed, the power consumption of PA and DAC
reduces and power amplifier efficiency increases due to reduction in
PAPR. Moreover, it has been shown that for a given BER performance
the requirement of Input-Backoff (IBO) reduces with reduction in
PAPR.
Abstract: In this paper, the construction of a detailed spine
model is presented using the LifeMOD Biomechanics Modeler. The
detailed spine model is obtained by refining spine segments in
cervical, thoracic and lumbar regions into individual vertebra
segments, using bushing elements representing the intervertebral
discs, and building various ligamentous soft tissues between
vertebrae. In the sagittal plane of the spine, constant force will be
applied from the posterior to anterior during simulation to determine
dynamic characteristics of the spine. The force magnitude is
gradually increased in subsequent simulations. Based on these
recorded dynamic properties, graphs of displacement-force
relationships will be established in terms of polynomial functions by
using the least-squares method and imported into a haptic integrated
graphic environment. A thoracolumbar spine model with complex
geometry of vertebrae, which is digitized from a resin spine
prototype, will be utilized in this environment. By using the haptic
technique, surgeons can touch as well as apply forces to the spine
model through haptic devices to observe the locomotion of the spine
which is computed from the displacement-force relationship graphs.
This current study provides a preliminary picture of our ongoing
work towards building and simulating bio-fidelity scoliotic spine
models in a haptic integrated graphic environment whose dynamic
properties are obtained from LifeMOD. These models can be helpful
for surgeons to examine kinematic behaviors of scoliotic spines and
to propose possible surgical plans before spine correction operations.
Abstract: This paper presents a procedure of forming the
mathematical model of radial electric power systems for simulation
of both transient and steady-state conditions. The research idea has
been based on nodal voltages technique and on differentiation of
Kirchhoff's current law (KCL) applied to each non-reference node of
the radial system, the result of which the nodal voltages has been
calculated by solving a system of algebraic equations. Currents of the
electric power system components have been determined by solving
their respective differential equations. Transforming the three-phase
coordinate system into Cartesian coordinate system in the model
decreased the overall number of equations by one third. The use of
Cartesian coordinate system does not ignore the DC component
during transient conditions, but restricts the model's implementation
for symmetrical modes of operation only. An example of the input
data for a four-bus radial electric power system has been calculated.
Abstract: Maximal Ratio Combining (MRC) is considered the most complex combining technique as it requires channel coefficients estimation. It results in the lowest bit error rate (BER) compared to all other combining techniques. However the BER starts to deteriorate as errors are introduced in the channel coefficients estimation. A novel combining technique, termed Generalized Maximal Ratio Combining (GMRC) with a polynomial kernel, yields an identical BER as MRC with perfect channel estimation and a lower BER in the presence of channel estimation errors. We show that GMRC outperforms the optimal MRC scheme in general and we hereinafter introduce it to the scientific community as a new “supraoptimal" algorithm. Since diversity combining is especially effective in small femto- and pico-cells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to IP-based 4th generation networks.
Abstract: this paper aims to provide an approach to predict the
performance of the product produced after multi-stages of
manufacturing processes, as well as the assembly. Such approach
aims to control and subsequently identify the relationship between
the process inputs and outputs so that a process engineer can more
accurately predict how the process output shall perform based on the
system inputs. The approach is guided by a six-sigma methodology to
obtain improved performance.
In this paper a case study of the manufacture of a hermetic
reciprocating compressor is presented. The application of artificial
neural networks (ANNs) technique is introduced to improve
performance prediction within this manufacturing environment. The
results demonstrate that the approach predicts accurately and
effectively.
Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over some
complex biological phenomena, such as problematic diseases like
cancer. This paper presents the latest authors- achievements regarding
the analysis of the networks of proteins (interactome networks), by
computing more efficiently the betweenness centrality measure. The
paper introduces the concept of betweenness centrality, and then
describes how betweenness computation can help the interactome net-
work analysis. Current sequential implementations for the between-
ness computation do not perform satisfactory in terms of execution
times. The paper-s main contribution is centered towards introducing
a speedup technique for the betweenness computation, based on
modified shortest path algorithms for sparse graphs. Three optimized
generic algorithms for betweenness computation are described and
implemented, and their performance tested against real biological
data, which is part of the IntAct dataset.
Abstract: Universities have an important role in social education in many aspects. In terms of creating awareness and convincing public about social issues, universities take a leading position for public. The best way to provide public support for social education is to develop public communication campaigns. The aim of this study is to present a public communication model which will be guided in social education practices. The study titled “Importance of public communication campaigns and art activities in Social Education “is based on the following topics: Effects of public communication campaigns on social education, Public relations techniques for education, communication strategies, Steps of public relations campaigns in social education, making persuasive messages for public communication campaigns, developing artistic messages and organizing art activities in social education. In addition to these topics, media planning for social education, forming a team as campaign managers, dialogues with opinion leaders in education and preparing creative communication models for social education will be taken into consideration. This study also aims to criticize social education Case studies in Turkey. At the same time, some communicative methods and principles will be given in the light of communication campaigns within the context of this notice.
Abstract: In metal cutting industries, mathematical/statistical
models are typically used to predict tool replacement time. These
off-line methods usually result in less than optimum replacement
time thereby either wasting resources or causing quality problems.
The few online real-time methods proposed use indirect measurement
techniques and are prone to similar errors. Our idea is based on
identifying the optimal replacement time using an electronic nose to
detect the airborne compounds released when the tool wear reaches
to a chemical substrate doped into tool material during the
fabrication. The study investigates the feasibility of the idea, possible
doping materials and methods along with data stream mining
techniques for detection and monitoring different phases of tool
wear.
Abstract: The primary objective of this paper was to construct a
“kinematic parameter-independent modeling of three-axis machine
tools for geometric error measurement" technique. Improving the
accuracy of the geometric error for three-axis machine tools is one of
the machine tools- core techniques. This paper first applied the
traditional method of HTM to deduce the geometric error model for
three-axis machine tools. This geometric error model was related to the
three-axis kinematic parameters where the overall errors was relative
to the machine reference coordinate system. Given that the
measurement of the linear axis in this model should be on the ideal
motion axis, there were practical difficulties. Through a measurement
method consolidating translational errors and rotational errors in the
geometric error model, we simplified the three-axis geometric error
model to a kinematic parameter-independent model. Finally, based on
the new measurement method corresponding to this error model, we
established a truly practical and more accurate error measuring
technique for three-axis machine tools.
Abstract: Shadows add great amount of realism to a scene and
many algorithms exists to generate shadows. Recently, Shadow
volumes (SVs) have made great achievements to place a valuable
position in the gaming industries. Looking at this, we concentrate on
simple but valuable initial partial steps for further optimization in SV
generation, i.e.; model simplification and silhouette edge detection
and tracking. Shadow volumes (SVs) usually takes time in generating
boundary silhouettes of the object and if the object is complex then
the generation of edges become much harder and slower in process.
The challenge gets stiffer when real time shadow generation and
rendering is demanded. We investigated a way to use the real time
silhouette edge detection method, which takes the advantage of
spatial and temporal coherence, and exploit the level-of-details
(LOD) technique for reducing silhouette edges of the model to use
the simplified version of the model for shadow generation speeding
up the running time. These steps highly reduce the execution time of
shadow volume generations in real-time and are easily flexible to any
of the recently proposed SV techniques. Our main focus is to exploit
the LOD and silhouette edge detection technique, adopting them to
further enhance the shadow volume generations for real time
rendering.
Abstract: MRAM technology provides a combination of fast
access time, non-volatility, data retention and endurance. While a
growing interest is given to two-terminal Magnetic Tunnel Junctions
(MTJ) based on Spin-Transfer Torque (STT) switching as the
potential candidate for a universal memory, its reliability is
dramatically decreased because of the common writing/reading path.
Three-terminal MTJ based on Spin-Orbit Torque (SOT) approach
revitalizes the hope of an ideal MRAM. It can overcome the
reliability barrier encountered in current two-terminal MTJs by
separating the reading and the writing path. In this paper, we study
two possible writing schemes for the SOT-MTJ device based on
recently fabricated samples. While the first is based on precessional
switching, the second requires the presence of permanent magnetic
field. Based on an accurate Verilog-A model, we simulate the two
writing techniques and we highlight advantages and drawbacks of
each one. Using the second technique, pioneering logic circuits based
on the three-terminal architecture of the SOT-MTJ described in this
work are under development with preliminary attractive results.