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: 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: The term interactive education indicates the meaning
related with multidisciplinary aspects of distance education following
contemporary means around a common basis with different
functional requirements. The aim of this paper is to reflect the new
techniques in education with the new methods and inventions. These
methods are better supplied by interactivity. The integration of
interactive facilities in the discipline of education with distance
learning is not a new concept but in addition the usage of these
methods on design issue is newly being adapted to design education.
In this paper the general approach of this method and after the
analysis of different samples, the advantages and disadvantages of
these approaches are being identified. The method of this paper is to
evaluate the related samples and then analyzing the main hypothesis.
The main focus is to mention the formation processes of this
education. Technological developments in education should be
filtered around the necessities of the design education and the
structure of the system could then be formed or renewed. The
conclusion indicates that interactive methods of education in design
issue is a meaning capturing not only technical and computational
intelligence aspects but also aesthetical and artistic approaches
coming together around the same purpose.
Abstract: The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).
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: 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: The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without affecting the classifier precision as it happens when only the NBC based on single words is retrained.
Abstract: This paper addresses parameter and state estimation problem in the presence of the perturbation of observer gain bounded input disturbances for the Lipschitz systems that are linear in unknown parameters and nonlinear in states. A new nonlinear adaptive resilient observer is designed, and its stability conditions based on Lyapunov technique are derived. The gain for this observer is derived systematically using linear matrix inequality approach. A numerical example is provided in which the nonlinear terms depend on unmeasured states. The simulation results are presented to show the effectiveness of the proposed method.
Abstract: The prediction of long-term deformations of concrete and reinforced concrete structures has been a field of extensive research and several different creep models have been developed so far. Most of the models were developed for constant concrete stresses, thus, in case of varying stresses a specific superposition principle or time-integration, respectively, is necessary. Nowadays, when modeling concrete creep the engineering focus is rather on the application of sophisticated time-integration methods than choosing the more appropriate creep model. For this reason, this paper presents a method to quantify the uncertainties of creep prediction originating from the selection of creep models or from the time-integration methods. By adapting variance based global sensitivity analysis, a methodology is developed to quantify the influence of creep model selection or choice of time-integration method. Applying the developed method, general recommendations how to model creep behavior for varying stresses are given.
Abstract: This paper presents a new adaptive impedance control
strategy, based on Function Approximation Technique (FAT) to
compensate for unknown non-flat environment shape or time-varying
environment location. The target impedance in the force controllable
direction is modified by incorporating adaptive compensators and the
uncertainties are represented by FAT, allowing the update law to be
derived easily. The force error feedback is utilized in the estimation
and the accurate knowledge of the environment parameters are not
required by the algorithm. It is shown mathematically that the
stability of the controller is guaranteed based on Lyapunov theory.
Simulation results presented to demonstrate the validity of the
proposed controller.
Abstract: In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.
Abstract: This paper introduces a new approach for the performance
analysis of adaptive filter with error saturation nonlinearity in
the presence of impulsive noise. The performance analysis of adaptive
filters includes both transient analysis which shows that how fast
a filter learns and the steady-state analysis gives how well a filter
learns. The recursive expressions for mean-square deviation(MSD)
and excess mean-square error(EMSE) are derived based on weighted
energy conservation arguments which provide the transient behavior
of the adaptive algorithm. The steady-state analysis for co-related
input regressor data is analyzed, so this approach leads to a new
performance results without restricting the input regression data to
be white.
Abstract: The contribution deals with analysis of identity style
at adolescents (N=463) at the age from 16 to 19 (the average age is
17,7 years). We used the Identity Style Inventory by Berzonsky,
distinguishing three basic, measured identity styles: informational,
normative, diffuse-avoidant identity style and also commitment. The
informational identity style influencing on personal adaptability,
coping strategies, quality of life and the normative identity style, it
means the style in which an individual takes on models of authorities
at self-defining were found to have the highest representation in the
studied group of adolescents by higher scores at girls in comparison
with boys. The normative identity style positively correlates with the
informational identity style. The diffuse-avoidant identity style was
found to be positively associated with maladaptive decisional
strategies, neuroticism and depressive reactions. There is the style,
in which the individual shifts aside defining his personality. In our
research sample the lowest score represents it and negatively
correlates with commitment, it means with coping strategies, thrust in
oneself and the surrounding world. The age of adolescents did not
significantly differentiate representation of identity style. We were
finding the model, in which informational and normative identity
style had positive relationship and the informational and diffuseavoidant
style had negative relationship, which were determinated
with commitment. In the same time the commitment is influenced
with other outside factors.
Abstract: In this paper we compare the response of linear and
nonlinear neural network-based prediction schemes in prediction of
received Signal-to-Interference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DS/CDMA) systems. The
nonlinear predictor is Multilayer Perceptron MLP and the linear
predictor is an Adaptive Linear (Adaline) predictor. We solve the
problem of complexity by using the Minimum Mean Squared Error
(MMSE) principle to select the optimal predictors. The optimized
Adaline predictor is compared to optimized MLP by employing
noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an
urban environment. The results show that the Adaline predictor can
estimates SIR with the same error as MLP when the user has the
velocity of 5 km/h and 60 km/h but by increasing the velocity up-to
120 km/h the mean squared error of MLP is two times more than
Adaline predictor. This makes the Adaline predictor (with lower
complexity) more suitable than MLP for closed-loop power control
where efficient and accurate identification of the time-varying
inverse dynamics of the multi path fading channel is required.
Abstract: Through the time, the higher education has changed
the learning system since mother tongue to bilingual, and in this new
century has been coming develop a multilingual education. All as
part of globalization process of the countries and the education.
Nevertheless, this change only has been effectively in countries of the
first world, the rest have been lagging. Therefore, these countries
require strengthen their higher education systems through models that
give way to multilingual and bilingual education. In this way, shows
a new model adapted from a systemic form to allow a higher
bilingual and multilingual education in Latin America. This
systematization aims to increase the skills and competencies
student’s, decrease the time learning of a second tongue, add to
multilingualism in the American Latin Universities, also, contribute
to position the region´s countries in a better global status, and
stimulate the development of new research in this area.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.