Abstract: Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.
Abstract: Providing authentication for the messages exchanged
between group members in addition to confidentiality is an important
issue in Secure Group communication. We develop a protocol for
Secure Authentic Communication where we address authentication
for the group communication scheme proposed by Blundo et al.
which only provides confidentiality. Authentication scheme used is a
multiparty authentication scheme which allows all the users in the
system to send and receive messages simultaneously. Our scheme is
secure against colluding malicious parties numbering fewer than k.
Abstract: Directive 2009/28/CE establishes, as obligatory objective, a share of renewable energies on energetic consumption of 20%, in European Union, in 2020 However, such European normative gives freedom to member states in the selection of the renewable promotion mechanism that allows them to obtain that objective. In this paper, we analyze the main characteristics of the promotion mechanisms of renewable energy used in the countries that shape the Electricity Iberian Market (Spain and Portugal) and the results in employment. The importance of these countries is given by the great increasing of the renewable energies which suppose a share higher than 30% of the overall generation in 2010. Therefore, this research paper can serve as the basis for the learning of other countries with regard to the main advantages that entail the use of a feed-in tariff system.
Abstract: This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.
Abstract: In this paper, the Fuzzy Autocatalytic Set (FACS) is
composed into Omega Algebra by embedding the membership value
of fuzzy edge connectivity using the property of transitive affinity.
Then, the Omega Algebra of FACS is a transformation semigroup
which is a special class of semigroup is shown.
Abstract: Using a set of confidence intervals, we develop a
common approach, to construct a fuzzy set as an estimator for
unknown parameters in statistical models. We investigate a method
to derive the explicit and unique membership function of such fuzzy
estimators. The proposed method has been used to derive the fuzzy
estimators of the parameters of a Normal distribution and some
functions of parameters of two Normal distributions, as well as the
parameters of the Exponential and Poisson distributions.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: Vitamin A deficiency is a public health problem in
Zimbabwe. Addressing vitamin A deficiency has the potential of
enhancing resistance to disease and reducing mortality especially in
children less than 5 years. We implemented and adapted vitamin A
outreach supplementation strategy within the National Immunization
Days and Extended Programme of Immunization in a rural district in
Zimbabwe. Despite usual operational challenges faced this approach
enabled the district to increase delivery of supplementation coverage.
This paper describes the outreach strategy that was implemented in
the remote rural district. The strategy covered 63 outreach sites with
2 sites being covered per day and visited once per month for the
whole year. Coverage reached 71% in an area of previous coverage
rates of around less than 50%. We recommend further exploration of
this strategy by others working in similar circumstances. This
strategy can be a potential way for use by Scaling-Up-Nutrition
member states.
Abstract: Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
Abstract: Kazakhstan attaches the great importance to
cooperation with European countries within the framework of
multilateral security organizations such as NATO. Cooperation of
Kazakhstan with the NATO is a prominent aspect of strengthening of
regional security of republic. It covers a wide spectrum of areas, such
as reform of sector of defense and security, military operative
compatibility of armed forces of NATO member-countries and
Kazakhstan, civil emergency planning and scientific cooperation. The
cooperation between Kazakhstan and NATO is based on the mutual
interests of neighboring republics in the region so that the existing
forms of cooperation between Kazakhstan and NATO will not be
negatively perceived both in Asia as well as among CIS countries.
Kazakhstan tailors its participation in the PfP programme through an
annual Individual Partnership Programme, selecting those activities
that will help achieve the goals it has set in the IPAP. Level of
cooperation within the limits of PfP essentially differs on each
republic. Cooperation with Kazakhstan progressed most of all since
has been signed IPAP from the NATO
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: In this paper we have suggested a new system for egovernment.
In this method a government can design a precise and
perfect system to control people and organizations by using five
major documents. These documents contain the important
information of each member of a society and help all organizations to
do their informatics tasks through them. This information would be
available by only a national code and a secure program would
support it. The suggested system can give a good awareness to the
society and help it be managed correctly.
Abstract: To understand life as biological system, evolutionary
understanding is indispensable. Protein interactions data are rapidly
accumulating and are suitable for system-level evolutionary analysis.
We have analyzed yeast protein interaction network by both
mathematical and biological approaches. In this poster presentation,
we inferred the evolutionary birth periods of yeast proteins by
reconstructing phylogenetic profile. It has been thought that hub
proteins that have high connection degree are evolutionary old. But
our analysis showed that hub proteins are entirely evolutionary new.
We also examined evolutionary processes of protein complexes. It
showed that member proteins of complexes were tend to have
appeared in the same evolutionary period. Our results suggested that
protein interaction network evolved by modules that form the
functional unit. We also reconstructed standardized phylogenetic trees
and calculated evolutionary rates of yeast proteins. It showed that
there is no obvious correlation between evolutionary rates and
connection degrees of yeast proteins.
Abstract: The object of this work is the probabilistic performance evaluation of safety instrumented systems (SIS), i.e. the average probability of dangerous failure on demand (PFDavg) and the average frequency of failure (PFH), taking into account the uncertainties related to the different parameters that come into play: failure rate (λ), common cause failure proportion (β), diagnostic coverage (DC)... This leads to an accurate and safe assessment of the safety integrity level (SIL) inherent to the safety function performed by such systems. This aim is in keeping with the requirement of the IEC 61508 standard with respect to handling uncertainty. To do this, we propose an approach that combines (1) Monte Carlo simulation and (2) fuzzy sets. Indeed, the first method is appropriate where representative statistical data are available (using pdf of the relating parameters), while the latter applies in the case characterized by vague and subjective information (using membership function). The proposed approach is fully supported with a suitable computer code.
Abstract: The Indian subcontinent is facing a massive challenge with regards to the energy security in member countries, i.e. providing a reliable source of electricity to facilitate development across various sectors of the economy and thereby achieve the developmental targets it has set for itself. A highly precarious situation exists in the subcontinent which is observed in the series of system failures which most of the times leads to system collapses-blackouts. To mitigate the issues related with energy security as well as keep in check the increasing supply demand gap, a possible solution that stands in front of the subcontinent is the deployment of an interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the sub continent as well as provide the infra structure for RES integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on VSC HVDC converters for the Supergrid modeling.
Abstract: This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Abstract: Background, measuring an individual-s Health
Literacy is gaining attention, yet no appropriate instrument is available
in Taiwan. Measurement tools that were developed and used in
western countries may not be appropriate for use in Taiwan due to a
different language system. Purpose of this research was to develop a
Health Literacy measurement instrument specific for Taiwan adults.
Methods, several experts of clinic physicians; healthcare
administrators and scholars identified 125 common used health related
Chinese phrases from major medical knowledge sources that easy
accessible to the public. A five-point Likert scale is used to measure
the understanding level of the target population. Such measurement is
then used to compare with the correctness of their answers to a health
knowledge test for validation. Samples, samples under study were
purposefully taken from four groups of people in the northern
Pingtung, OPD patients, university students, community residents,
and casual visitors to the central park. A set of health knowledge index
with 10 questions is used to screen those false responses. A sample
size of 686 valid cases out of 776 was then included to construct this
scale. An independent t-test was used to examine each individual
phrase. The phrases with the highest significance are then identified
and retained to compose this scale. Result, a Taiwan Health Literacy
Scale (THLS) was finalized with 66 health-related phrases under nine
divisions. Cronbach-s alpha of each division is at a satisfactory level
of 89% and above. Conclusions, factors significantly differentiate the
levels of health literacy are education, female gender, age, family
members of stroke victims, experience with patient care, and
healthcare professionals in the initial application in this study..
Abstract: Having done in this study, air-conditioning
automation for patisserie shopwindow was designed. In the cooling
sector it is quite important to cooling up the air temperature in the
shopwindow within short time interval. Otherwise the patisseries
inside of the shopwindow will be spoilt in a few days. Additionally
the humidity is other important parameter for the patisseries kept in
shopwindow. It must be raised up to desired level in a quite short
time. Traditional patisserie shopwindows only allow controlling
temperature manually. There is no humidity control and humidity is
supplied by fans that are directed to the water at the bottom of the
shopwindows. In this study, humidity and temperature sensors
(SHT11), PIC, AC motor controller, DC motor controller, ultrasonic
nebulizer and other electronic circuit members were used to simulate
air conditioning automation for patisserie shopwindow in proteus
software package. The simulation results showed that temperature
and humidity values are adjusted in desired time duration by openloop
control technique. Outer and inner temperature and humidity
values were used for control mechanism.
Abstract: Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.