Abstract: The client server systems using mobile
communications networks for data transmission became very
attractive for many economic agents, in the purpose of promoting and
offering electronic services to their clients. E-services are suitable for
business developing and financial benefits increasing. The products
or services can be efficiently delivered to a large number of clients,
using mobile Internet access technologies. The clients can have
access to e-services, anywhere and anytime, with the support of 3G,
GPRS, WLAN, etc., channels bandwidth, data services and protocols.
Based on the mobile communications networks evolution and
development, a convergence of technological and financial interests
of mobile operators, software developers, mobile terminals producers
and e-content providers is established. These will lead to a high level
of integration of IT&C resources and will facilitate the value added
services delivery through the mobile communications networks. In
this paper it is presented a client server system, for e-services access,
with Smartphones and PDA-s mobile software applications, installed
on Symbian and Windows Mobile operating systems.
Abstract: This paper proposes a novel feature extraction method,
based on Discrete Wavelet Transform (DWT) and K-L Seperability
(KLS), for the classification of Functional Data (FD). This method
combines the decorrelation and reduction property of DWT and the
additive independence property of KLS, which is helpful to extraction
classification features of FD. It is an advanced approach of the
popular wavelet based shrinkage method for functional data reduction
and classification. A theory analysis is given in the paper to prove the
consistent convergence property, and a simulation study is also done
to compare the proposed method with the former shrinkage ones. The
experiment results show that this method has advantages in improving
classification efficiency, precision and robustness.
Abstract: Due to their high power-to-weight ratio and low cost,
pneumatic actuators are attractive for robotics and automation
applications; however, achieving fast and accurate control of their
position have been known as a complex control problem. A
methodology for obtaining high position accuracy with a linear
pneumatic actuator is presented. During experimentation with a
number of PID classical control approaches over many operations of
the pneumatic system, the need for frequent manual re-tuning of the
controller could not be eliminated. The reason for this problem is
thermal and energy losses inside the cylinder body due to the
complex friction forces developed by the piston displacements.
Although PD controllers performed very well over short periods, it
was necessary in our research project to introduce some form of
automatic gain-scheduling to achieve good long-term performance.
We chose a fuzzy logic system to do this, which proved to be an
easily designed and robust approach. Since the PD approach showed
very good behaviour in terms of position accuracy and settling time,
it was incorporated into a modified form of the 1st order Tagaki-
Sugeno fuzzy method to build an overall controller. This fuzzy gainscheduler
uses an input variable which automatically changes the PD
gain values of the controller according to the frequency of repeated
system operations. Performance of the new controller was
significantly improved and the need for manual re-tuning was
eliminated without a decrease in performance. The performance of
the controller operating with the above method is going to be tested
through a high-speed web network (GRID) for research purposes.
Abstract: In a wind power generator using doubly fed induction
generator (DFIG), the three-phase pulse width modulation (PWM)
voltage source converter (VSC) is used as grid side converter (GSC)
and rotor side converter (RSC). The standard linear control laws
proposed for GSC provides not only instablity against comparatively
large-signal disturbances, but also the problem of stability due to
uncertainty of load and variations in parameters. In this paper, a
nonlinear controller is designed for grid side converter (GSC) of a
DFIG for wind power application. The nonlinear controller is
designed based on the input-output feedback linearization control
method. The resulting closed-loop system ensures a sufficient
stability region, make robust to variations in circuit parameters and
also exhibits good transient response. Computer simulations and
experimental results are presented to confirm the effectiveness of the
proposed control strategy.
Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS algorithm are comparable with those of transform domain LMS(TDLMS) algorithm. Simulation results show efficiency and robustness of the proposed algorithm with reduced computational complexity.
Abstract: This paper proposes the study of a robust control of
the doubly fed induction generator (DFIG) used in a wind energy
production. The proposed control is based on the linear active
disturbance rejection control (ADRC) and it is applied to the control
currents rotor of the DFIG, the DC bus voltage and active and
reactive power exchanged between the DFIG and the network. The
system under study and the proposed control are simulated using
MATLAB/SIMULINK.
Abstract: We introduce an effective approach for automatic offline au- thentication of handwritten samples where the forgeries are skillfully done, i.e., the true and forgery sample appearances are almost alike. Subtle details of temporal information used in online verification are not available offline and are also hard to recover robustly. Thus the spatial dynamic information like the pen-tip pressure characteristics are considered, emphasizing on the extraction of low density pixels. The points result from the ballistic rhythm of a genuine signature which a forgery, however skillful that may be, always lacks. Ten effective features, including these low density points and den- sity ratio, are proposed to make the distinction between a true and a forgery sample. An adaptive decision criteria is also derived for better verification judgements.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.
Abstract: Web applications have become complex and crucial for many firms, especially when combined with areas such as CRM (Customer Relationship Management) and BPR (Business Process Reengineering). The scientific community has focused attention to Web application design, development, analysis, testing, by studying and proposing methodologies and tools. Static and dynamic techniques may be used to analyze existing Web applications. The use of traditional static source code analysis may be very difficult, for the presence of dynamically generated code, and for the multi-language nature of the Web. Dynamic analysis may be useful, but it has an intrinsic limitation, the low number of program executions used to extract information. Our reverse engineering analysis, used into our WAAT (Web Applications Analysis and Testing) project, applies mutational techniques in order to exploit server side execution engines to accomplish part of the dynamic analysis. This paper studies the effects of mutation source code analysis applied to Web software to build application models. Mutation-based generated models may contain more information then necessary, so we need a pruning mechanism.
Abstract: Environmental awareness and the recent
environmental policies have forced many electric utilities to
restructure their operational practices to account for their emission
impacts. One way to accomplish this is by reformulating the
traditional economic dispatch problem such that emission effects are
included in the mathematical model. This paper presents a Particle
Swarm Optimization (PSO) algorithm to solve the Economic-
Emission Dispatch problem (EED) which gained recent attention due
to the deregulation of the power industry and strict environmental
regulations. The problem is formulated as a multi-objective one with
two competing functions, namely economic cost and emission
functions, subject to different constraints. The inequality constraints
considered are the generating unit capacity limits while the equality
constraint is generation-demand balance. A novel equality constraint
handling mechanism is proposed in this paper. PSO algorithm is
tested on a 30-bus standard test system. Results obtained show that
PSO algorithm has a great potential in handling multi-objective
optimization problems and is capable of capturing Pareto optimal
solution set under different loading conditions.
Abstract: With deep development of software reuse, componentrelated
technologies have been widely applied in the development of
large-scale complex applications. Component identification (CI) is
one of the primary research problems in software reuse, by analyzing
domain business models to get a set of business components with high
reuse value and good reuse performance to support effective reuse.
Based on the concept and classification of CI, its technical stack is
briefly discussed from four views, i.e., form of input business models,
identification goals, identification strategies, and identification
process. Then various CI methods presented in literatures are
classified into four types, i.e., domain analysis based methods,
cohesion-coupling based clustering methods, CRUD matrix based
methods, and other methods, with the comparisons between these
methods for their advantages and disadvantages. Additionally, some
insufficiencies of study on CI are discussed, and the causes are
explained subsequently. Finally, it is concluded with some
significantly promising tendency about research on this problem.
Abstract: This article is an extension and a practical application
approach of Wheeler-s NEBIC theory (Net Enabled Business
Innovation Cycle). NEBIC theory is a new approach in IS research
and can be used for dynamic environment related to new technology.
Firms can follow the market changes rapidly with support of the IT
resources. Flexible firms adapt their market strategies, and respond
more quickly to customers changing behaviors. When every leading
firm in an industry has access to the same IT resources, the way that
these IT resources are managed will determine the competitive
advantages or disadvantages of firm. From Dynamic Capabilities
Perspective and from newly introduced NEBIC theory by Wheeler,
we know that only IT resources cannot deliver customer value but
good configuration of those resources can guarantee customer value
by choosing the right emerging technology, grasping the economic
opportunities through business innovation and growth. We found
evidences in literature that SOA (Service Oriented Architecture) is a
promising emerging technology which can deliver the desired
economic opportunity through modularity, flexibility and loosecoupling.
SOA can also help firms to connect in network which can
open a new window of opportunity to collaborate in innovation and
right kind of outsourcing
Abstract: Product Data Management (PDM) systems for Computer
Aided Design (CAD) file management are widely established
in design processes. This management system is indispensable for
design collaboration or when design task distribution is present. It is
thus surprising that engineering design curricula has not paid much
attention in the education of PDM systems. This is also the case
for eduction of ecodesign and environmental evaluation of products.
With the rise of sustainability as a strategic aspect in companies,
environmental concerns are becoming a key issue in design. This
paper discusses the establishment of a PDM platform to be used
among technical and vocational schools in Austria. The PDM system
facilitates design collaboration among these schools. Further, it will
be discussed how the PDM system has been prepared in order to
facilitate environmental evaluation of parts, components and subassemblies
of a product. By integrating a Business Intelligence
solution, environmental Life Cycle Assessment and communication
of results is enabled.
Abstract: This article examines the emergence and development
of the Kazakhstan species of humanism. The biggest challenge for
Kazakhstan in terms of humanism is connected with advocating
human values in parallel to promoting national interests; preserving
the continuity of traditions in various spheres of life, business and
culture. This should be a common goal for the entire society, the
main direction for a national intelligence, and a platform for the state
policy. An idea worth considering is a formation of national humanist
tradition model; the challenges are adapting people to live in the
context of new industrial and innovative economic conditions,
keeping the balance during intensive economic development of the
country, and ensuring social harmony in the society.
Abstract: In this paper we present an enhanced noise reduction method for robust speech recognition using Adaptive Gain Equalizer with Non linear Spectral Subtraction. In Adaptive Gain Equalizer method (AGE), the input signal is divided into a number of subbands that are individually weighed in time domain, in accordance to the short time Signal-to-Noise Ratio (SNR) in each subband estimation at every time instant. Instead of focusing on suppression the noise on speech enhancement is focused. When analysis was done under various noise conditions for speech recognition, it was found that Adaptive Gain Equalizer method algorithm has an obvious failing point for a SNR of -5 dB, with inadequate levels of noise suppression for SNR less than this point. This work proposes the implementation of AGE when coupled with Non linear Spectral Subtraction (AGE-NSS) for robust speech recognition. The experimental result shows that out AGE-NSS performs the AGE when SNR drops below -5db level.
Abstract: In this paper, a novel feature-based image
watermarking scheme is proposed. Zernike moments which have
invariance properties are adopted in the scheme. In the proposed
scheme, feature points are first extracted from host image and several
circular patches centered on these points are generated. The patches
are used as carriers of watermark information because they can be
regenerated to locate watermark embedding positions even when
watermarked images are severely distorted. Zernike transform is then
applied to the patches to calculate local Zernike moments. Dither
modulation is adopted to quantize the magnitudes of the Zernike
moments followed by false alarm analysis. Experimental results show
that quality degradation of watermarked image is visually
transparent. The proposed scheme is very robust against image
processing operations and geometric attacks.
Abstract: The Sensor Network consists of densely deployed
sensor nodes. Energy optimization is one of the most important
aspects of sensor application design. Data acquisition and aggregation
techniques for processing data in-network should be energy efficient.
Due to the cross-layer design, resource-limited and noisy nature
of Wireless Sensor Networks(WSNs), it is challenging to study
the performance of these systems in a realistic setting. In this
paper, we propose optimizing queries by aggregation of data and
data redundancy to reduce energy consumption without requiring
all sensed data and directed diffusion communication paradigm to
achieve power savings, robust communication and processing data
in-network. To estimate the per-node power consumption POWERTossim
mica2 energy model is used, which provides scalable and
accurate results. The performance analysis shows that the proposed
methods overcomes the existing methods in the aspects of energy
consumption in wireless sensor networks.
Abstract: The need for micromechanical inertial sensors is increasing
in future electronic stability control (ESC) and other positioning,
navigation and guidance systems. Due to the rising density of
sensors in automotive and consumer devices the goal is not only to get
high performance, robustness and smaller package sizes, but also to
optimize the energy management of the overall sensor system. This
paper presents an evaluation concept for a surface micromachined
yaw rate sensor. Within this evaluation concept an energy-efficient
operation of the drive mode of the yaw rate sensor is enabled. The
presented system concept can be realized within a power management
subsystem.