Abstract: Active Power Filters (APFs) are today the most
widely used systems to eliminate harmonics compensate power
factor and correct unbalanced problems in industrial power plants.
We propose to improve the performances of conventional APFs by
using artificial neural networks (ANNs) for harmonics estimation.
This new method combines both the strategies for extracting the
three-phase reference currents for active power filters and DC link
voltage control method. The ANNs learning capabilities to
adaptively choose the power system parameters for both to compute
the reference currents and to recharge the capacitor value requested
by VDC voltage in order to ensure suitable transit of powers to
supply the inverter. To investigate the performance of this
identification method, the study has been accomplished using
simulation with the MATLAB Simulink Power System Toolbox. The
simulation study results of the new (SAPF) identification technique
compared to other similar methods are found quite satisfactory by
assuring good filtering characteristics and high system stability.
Abstract: A subjectively influenced router for vehicles in a fourjunction
traffic system is presented. The router is based on a 3-layer
Backpropagation Neural Network (BPNN) and a greedy routing
procedure. The BPNN detects priorities of vehicles based on the
subjective criteria. The subjective criteria and the routing procedure
depend on the routing plan towards vehicles depending on the user.
The routing procedure selects vehicles from their junctions based on
their priorities and route them concurrently to the traffic system. That
is, when the router is provided with a desired vehicles selection
criteria and routing procedure, it routes vehicles with a reasonable
junction clearing time. The cost evaluation of the router determines
its efficiency. In the case of a routing conflict, the router will route
the vehicles in a consecutive order and quarantine faulty vehicles.
The simulations presented indicate that the presented approach is an
effective strategy of structuring a subjective vehicle router.
Abstract: The paper proposes a new concept in developing
collaborative design system. The concept framework involves
applying simulation of supply chain management to collaborative
design called – 'SCM–Based Design Tool'. The system is developed
particularly to support design activities and to integrate all facilities
together. The system is aimed to increase design productivity and
creativity. Therefore, designers and customers can collaborate by the
system since conceptual design. JAG: Jewelry Art Generator based
on artificial intelligence techniques is integrated into the system.
Moreover, the proposed system can support users as decision tool
and data propagation. The system covers since raw material supply
until product delivery. Data management and sharing information are
visually supported to designers and customers via user interface. The
system is developed on Web–assisted product development
environment. The prototype system is presented for Thai jewelry
industry as a system prototype demonstration, but applicable for
other industry.
Abstract: This paper provides an overview of auction theory
literature. We present a general review on literature of various
auctions and focus ourselves specifically on an English auction. We
are interested in modelling bidder-s behavior in an English auction
environment. And hence, we present an overview of the New Zealand
wool auction followed by a model that would describe a bidder-s
decision making behavior from the New Zealand wool auction. The
mathematical assumptions in an English auction environment are
demonstrated from the perspective of the New Zealand wool auction.
Abstract: Existing methods in which the animation data of all frames are stored and reproduced as with vertex animation cannot be used in mobile device environments because these methods use large amounts of the memory. So 3D animation data reduction methods aimed at solving this problem have been extensively studied thus far and we propose a new method as follows. First, we find and remove frames in which motion changes are small out of all animation frames and store only the animation data of remaining frames (involving large motion changes). When playing the animation, the removed frame areas are reconstructed using the interpolation of the remaining frames. Our key contribution is to calculate the accelerations of the joints of individual frames and the standard deviations of the accelerations using the information of joint locations in the relevant 3D model in order to find and delete frames in which motion changes are small. Our methods can reduce data sizes by approximately 50% or more while providing quality which is not much lower compared to original animations. Therefore, our method is expected to be usefully used in mobile device environments or other environments in which memory sizes are limited.
Abstract: Steel surface defect detection is essentially one of
pattern recognition problems. Support Vector Machines (SVMs) are
known as one of the most proper classifiers in this application. In this
paper, we introduce a more accurate classification method by using
SVMs as our final classifier of the inspection system. In this scheme,
multiclass classification task is performed based on the "one-againstone"
method and different kernels are utilized for each pair of the
classes in multiclass classification of the different defects.
In the proposed system, a decision tree is employed in the first
stage for two-class classification of the steel surfaces to "defect" and
"non-defect", in order to decrease the time complexity. Based on
the experimental results, generated from over one thousand images,
the proposed multiclass classification scheme is more accurate than
the conventional methods and the overall system yields a sufficient
performance which can meet the requirements in steel manufacturing.
Abstract: This paper focuses on a novel method for semantic
searching and retrieval of information about learning materials.
Metametadata encapsulate metadata instances by using the properties
and attributes provided by ontologies rather than describing learning
objects. A novel metametadata taxonomy has been developed which
provides the basis for a semantic search engine to extract, match and
map queries to retrieve relevant results. The use of ontological views
is a foundation for viewing the pedagogical content of metadata
extracted from learning objects by using the pedagogical attributes
from the metametadata taxonomy. Using the ontological approach
and metametadata (based on the metametadata taxonomy) we present
a novel semantic searching mechanism.These three strands – the
taxonomy, the ontological views, and the search algorithm – are
incorporated into a novel architecture (OMESCOD) which has been
implemented.
Abstract: In MPEG and H.26x standards, to eliminate the
temporal redundancy we use motion estimation. Given that the
motion estimation stage is very complex in terms of computational
effort, a hardware implementation on a re-configurable circuit is
crucial for the requirements of different real time multimedia
applications. In this paper, we present hardware architecture for
motion estimation based on "Full Search Block Matching" (FSBM)
algorithm. This architecture presents minimum latency, maximum
throughput, full utilization of hardware resources such as embedded
memory blocks, and combining both pipelining and parallel
processing techniques. Our design is described in VHDL language,
verified by simulation and implemented in a Stratix II
EP2S130F1020C4 FPGA circuit. The experiment result show that the
optimum operating clock frequency of the proposed design is 89MHz
which achieves 160M pixels/sec.
Abstract: In this note, the robust static output feedback
stabilisation of an induction machine is addressed. The machine is
described by a non homogenous bilinear model with structural
uncertainties, and the feedback gain is computed via an iterative LMI
(ILMI) algorithm.
Abstract: In this contribution a newly developed e-learning environment is presented, which incorporates Intelligent Agents and Computational Intelligence Techniques. The new e-learning environment is constituted by three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. These parts are distributed geographically in dispersed computer servers, with main focus on the design and development of these subsystems through the use of new and emerging technologies. These parts are interconnected in an interoperable way, using web services for the integration of the subsystems, in order to enhance the user modelling procedure and achieve the goals of the learning process.
Abstract: techniques are examined to overcome the
performance degradation caused by the channel dispersion using
slow frequency hopping (SFH) with dynamic frequency hopping
(DFH) pattern adaptation. In DFH systems, the frequency slots are
selected by continuous quality monitoring of all frequencies available
in a system and modification of hopping patterns for each individual
link based on replacing slots which its signal to interference ratio
(SIR) measurement is below a required threshold. Simulation results
will show the improvements in BER obtained by DFH in comparison
with matched frequency hopping (MFH), random frequency hopping
(RFH) and multi-carrier code division multiple access (MC-CDMA)
in multipath slowly fading dispersive channels using a generalized
bandpass two-path transfer function model, and will show the
improvement obtained according to the threshold selection.
Abstract: In this article an isotropic linear elastic half-space with
a cylindrical cavity of finite length is considered to be under the
effect of a ring shape time-harmonic torsion force applied at an
arbitrary depth on the surface of the cavity. The equation of
equilibrium has been written in a cylindrical coordinate system. By
means of Fourier cosine integral transform, the non-zero
displacement component is obtained in the transformed domain. With
the aid of the inversion theorem of the Fourier cosine integral
transform, the displacement is obtained in the real domain. With the
aid of boundary conditions, the involved boundary value problem for
the fundamental solution is reduced to a generalized Cauchy singular
integral equation. Integral representation of the stress and
displacement are obtained, and it is shown that their degenerated
form to the static problem coincides with existing solutions in the
literature.
Abstract: Studies on the distribution of traffic demands have
been proceeding by providing traffic information for reducing
greenhouse gases and reinforcing the road's competitiveness in the
transport section, however, since it is preferentially required the
extensive studies on the driver's behavior changing routes and its
influence factors, this study has been developed a discriminant model
for changing routes considering driving conditions including traffic
conditions of roads and driver's preferences for information media. It
is divided into three groups depending on driving conditions in group
classification with the CART analysis, which is statistically
meaningful. And the extent that driving conditions and preferred
media affect a route change is examined through a discriminant
analysis, and it is developed a discriminant model equation to predict a
route change. As a result of building the discriminant model equation,
it is shown that driving conditions affect a route change much more,
the entire discriminant hit ratio is derived as 64.2%, and this
discriminant equation shows high discriminant ability more than a
certain degree.
Abstract: The quick training algorithms and accurate solution
procedure for incremental learning aim at improving the efficiency of
training of SVR, whereas there are some disadvantages for them, i.e.
the nonconvergence of the formers for changeable training set and
the inefficiency of the latter for a massive dataset. In order to handle
the problems, a new training algorithm for a changeable training
set, named Approximation Incremental Training Algorithm (AITA),
was proposed. This paper explored the reason of nonconvergence
theoretically and discussed the realization of AITA, and finally
demonstrated the benefits of AITA both on precision and efficiency.
Abstract: Within this work High Temperature Single Impact
Studies were performed to evaluate deformation mechanisms at
different energy and momentum levels. To show the influence of
different microstructures and hardness levels and their response to
single impacts four different materials were tested at various
temperatures up to 700°C. One carbide reinforced NiCrBSi based
Metal Matrix Composite and three different steels were tested. The
aim of this work is to determine critical energies for fracture
appearance and the materials response at different energy and
momenta levels. Critical impact loadings were examined at elevated
temperatures to limit operating conditions in impact dominated
regimes at elevated temperatures. The investigations on the
mechanisms were performed using different means of microscopy at
the surface and in metallographic cross sections. Results indicate
temperature dependence of the occurrence of cracks in hardphase
rich materials, such as Metal Matrix Composites High Speed Steels
and the influence of different impact momenta at constant energies
on the deformation of different steels.
Abstract: Management Systems are powerful tools for businesses
to manage quality , environmental and occupational health and safety
requirements . where once these systems were considered as stand
alone control mechanisms , industry is now opting to increase the
efficiency of these documented systems through a more integrated
approach . System integration offers a significant step forward, where
there are similarities between system components , reducing
duplication and adminstration costs and increasing efficiency .
At first , this paper reviews integrated management system structure
and its benefits. The second part of this paper focuses on the one
example implementation of such a system at Imam Khomeini
Hospital and in final part of the paper will be discuss outcomes of
that proccess .
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: Although there have been many researches in cluster
analysis to consider on feature weights, little effort is made on sample
weights. Recently, Yu et al. (2011) considered a probability
distribution over a data set to represent its sample weights and then
proposed sample-weighted clustering algorithms. In this paper, we
give a sample-weighted version of generalized fuzzy clustering
regularization (GFCR), called the sample-weighted GFCR
(SW-GFCR). Some experiments are considered. These experimental
results and comparisons demonstrate that the proposed SW-GFCR is
more effective than the most clustering algorithms.
Abstract: Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.
Abstract: Public sector corruption has long-term and damaging
effects that are deep and broad. Addressing corruption relies on
understanding the drivers that precipitate acts of corruption and
developing educational programs that target areas of vulnerability.
This paper provides an innovative approach to explore the nature of
corruption by drawing on the perceptions and ideas of a group of
public servants who have been part of a corruption investigation. The
paper examines these reflections through the ideas of Pierre Bourdieu
and Alfred Schutz to point to some of the steps that can lead to
corrupt activity. The paper demonstrates that phenomenological
inquiry is useful in the exploration of corruption and, as a theoretical
framework, it highlights that corruption emerges through a
combination of conflict, doubt and uncertainty. The paper calls for
anti-corruption education programs to be attentive to way in which
these conditions can influence the steps into corruption.