Abstract: This paper presents a new method to design nonlinear
feedback linearization controller for PEMFCs (Polymer Electrolyte
Membrane Fuel Cells). A nonlinear controller is designed based on
nonlinear model to prolong the stack life of PEMFCs. Since it is
known that large deviations between hydrogen and oxygen partial
pressures can cause severe membrane damage in the fuel cell,
feedback linearization is applied to the PEMFC system so that the
deviation can be kept as small as possible during disturbances or load
variations. To obtain an accurate feedback linearization controller,
tuning the linear parameters are always important. So in proposed
study NSGA (Non-Dominated Sorting Genetic Algorithm)-II method
was used to tune the designed controller in aim to decrease the
controller tracking error. The simulation result showed that the
proposed method tuned the controller efficiently.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: Cognitive Radio is a turning out technology that
empowers viable usage of the spectrum. Energy Detector-based
Sensing is the most broadly utilized spectrum sensing strategy.
Besides, it's a lot of generic as receivers doesn't would like any
information on the primary user's signals, channel data, of even the
sort of modulation. This paper puts forth the execution of energy
detection sensing for AM (Amplitude Modulated) signal at 710 KHz,
FM (Frequency Modulated) signal at 103.45 MHz (local station
frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz.
The OFDM/OFDMA based WiMAX physical layer with
convolutional channel coding is actualized utilizing USRP N210
(Universal Software Radio Peripheral) and GNU Radio based
Software Defined Radio (SDR). Test outcomes demonstrated the
BER (Bit Error Rate) augmentation with channel noise and BER
execution is dissected for different Eb/N0 (the energy per bit to noise
power spectral density ratio) values.
Abstract: The paper presents combined automatic speech
recognition (ASR) of English and machine translation (MT) for
English and Croatian and Croatian-English language pairs in the
domain of business correspondence. The first part presents results of
training the ASR commercial system on English data sets, enriched
by error analysis. The second part presents results of machine
translation performed by free online tool for English and Croatian
and Croatian-English language pairs. Human evaluation in terms of
usability is conducted and internal consistency calculated by
Cronbach's alpha coefficient, enriched by error analysis. Automatic
evaluation is performed by WER (Word Error Rate) and PER
(Position-independent word Error Rate) metrics, followed by
investigation of Pearson’s correlation with human evaluation.
Abstract: In this paper, we have proposed a numerical method
for solving fuzzy Fredholm integral equation of the second kind. In
this method a combination of orthonormal Bernstein and Block-Pulse
functions are used. In most cases, the proposed method leads to
the exact solution. The advantages of this method are shown by an
example and calculate the error analysis.
Abstract: Anultra-low power capacitor less low-dropout voltage
regulator with improved transient response using gain enhanced feed
forward path compensation is presented in this paper. It is based on a
cascade of a voltage amplifier and a transconductor stage in the feed
forward path with regular error amplifier to form a composite gainenhanced
feed forward stage. It broadens the gain bandwidth and thus
improves the transient response without substantial increase in power
consumption. The proposed LDO, designed for a maximum output
current of 100 mA in UMC 180 nm, requires a quiescent current of
69 )A. An undershot of 153.79mV for a load current changes from
0mA to 100mA and an overshoot of 196.24mV for current change of
100mA to 0mA. The settling time is approximately 1.1 )s for the
output voltage undershooting case. The load regulation is of 2.77
)V/mA at load current of 100mA. Reference voltage is generated by
using an accurate band gap reference circuit of 0.8V.The costly
features of SOC such as total chip area and power consumption is
drastically reduced by the use of only a total compensation
capacitance of 6pF while consuming power consumption of 0.096
mW.
Abstract: In this study, a comparative analysis of the approaches
associated with the use of neural network algorithms for effective
solution of a complex inverse problem – the problem of identifying
and determining the individual concentrations of inorganic salts in
multicomponent aqueous solutions by the spectra of Raman
scattering of light – is performed. It is shown that application of
artificial neural networks provides the average accuracy of
determination of concentration of each salt no worse than 0.025 M.
The results of comparative analysis of input data compression
methods are presented. It is demonstrated that use of uniform
aggregation of input features allows decreasing the error of
determination of individual concentrations of components by 16-18%
on the average.
Abstract: A method is proposed for stable detection of
seismoacoustic sources in C-OTDR systems that guarantee given
upper bounds for probabilities of type I and type II errors. Properties
of the proposed method are rigorously proved. The results of
practical applications of the proposed method in a real C-OTDRsystem
are presented.
Abstract: The generalized wave equation models various
problems in sciences and engineering. In this paper, a new three-time
level implicit approach based on cubic trigonometric B-spline for the
approximate solution of wave equation is developed. The usual finite
difference approach is used to discretize the time derivative while
cubic trigonometric B-spline is applied as an interpolating function in
the space dimension. Von Neumann stability analysis is used to
analyze the proposed method. Two problems are discussed to exhibit
the feasibility and capability of the method. The absolute errors and
maximum error are computed to assess the performance of the
proposed method. The results were found to be in good agreement
with known solutions and with existing schemes in literature.
Abstract: One of the major goals of Spoken Dialog Systems
(SDS) is to understand what the user utters.
In the SDS domain, the Spoken Language Understanding (SLU)
Module classifies user utterances by means of a pre-definite
conceptual knowledge. The SLU module is able to recognize only the
meaning previously included in its knowledge base. Due the vastity
of that knowledge, the information storing is a very expensive
process.
Updating and managing the knowledge base are time-consuming
and error-prone processes because of the rapidly growing number of
entities like proper nouns and domain-specific nouns. This paper
proposes a solution to the problem of Name Entity Recognition
(NER) applied to a SDS domain. The proposed solution attempts to
automatically recognize the meaning associated with an utterance by
using the PANKOW (Pattern based Annotation through Knowledge
On the Web) method at runtime.
The method being proposed extracts information from the Web to
increase the SLU knowledge module and reduces the development
effort. In particular, the Google Search Engine is used to extract
information from the Facebook social network.
Abstract: It is known that residual welding deformations give
negative effect to processability and operational quality of welded
structures, complicating their assembly and reducing strength.
Therefore, selection of optimal technology, ensuring minimum
welding deformations, is one of the main goals in developing a
technology for manufacturing of welded structures.
Through years, JSC SSTC has been developing a theory for
estimation of welding deformations and practical activities for
reducing and compensating such deformations during welding
process. During long time a methodology was used, based on analytic
dependence. This methodology allowed defining volumetric changes
of metal due to welding heating and subsequent cooling. However,
dependences for definition of structures deformations, arising as a
result of volumetric changes of metal in the weld area, allowed
performing calculations only for simple structures, such as units, flat
sections and sections with small curvature. In case of complex 3D
structures, estimations on the base of analytic dependences gave
significant errors.
To eliminate this shortage, it was suggested to use finite elements
method for resolving of deformation problem. Here, one shall first
calculate volumes of longitudinal and transversal shortenings of
welding joints using method of analytic dependences and further,
with obtained shortenings, calculate forces, which action is
equivalent to the action of active welding stresses. Further, a finiteelements
model of the structure is developed and equivalent forces
are added to this model. Having results of calculations, an optimal
sequence of assembly and welding is selected and special measures to
reduce and compensate welding deformations are developed and
taken.
Abstract: This paper proposes a cooperative Alamouti space time
transmission scheme with low relay complexity for the cooperative
communication systems. In the proposed scheme, the source node
combines the data symbols to construct the Alamouti-coded form at
the destination node, while the conventional scheme performs the
corresponding operations at the relay nodes. In simulation results,
it is shown that the proposed scheme achieves the second order
cooperative diversity while maintaining the same bit error rate (BER)
performance as that of the conventional scheme.
Abstract: This paper presents a regression model with
autocorrelated errors in which the inputs are social moods obtained by
analyzing the adjectives in Twitter posts using a document topic
model, where document topics are extracted using LDA. The
regression model predicts Dow Jones Industrial Average (DJIA) more
precisely than autoregressive moving-average models.
Abstract: In this paper, we apply the Exp-function method to
Rosenau-Kawahara and Rosenau-KdV equations. Rosenau-Kawahara
equation is the combination of the Rosenau and standard Kawahara
equations and Rosenau-KdV equation is the combination of the
Rosenau and standard KdV equations. These equations are nonlinear
partial differential equations (NPDE) which play an important role
in mathematical physics. Exp-function method is easy, succinct and
powerful to implement to nonlinear partial differential equations
arising in mathematical physics. We mainly try to present an
application of Exp-function method and offer solutions for common
errors wich occur during some of the recent works.
Abstract: Analytical expressions of the current and angular errors, as well as the frequency characteristics of an induction converter describing the relation with its structural parameters, the core and winding characteristics are obtained. Based on estimation of the dependences obtained, a mathematical problem of parametric optimization is formulated which can successfully be used for investigating and diagnosing an induction converter.
Abstract: Developing a reliable and sustainable software products is today a big challenge among up–coming software developers in Nigeria. The inability to develop a comprehensive problem statement needed to execute proper requirements engineering process is missing. The need to describe the ‘what’ of a system in one document, written in a natural language is a major step in the overall process of Software Engineering. Requirements Engineering is a process use to discover, analyze and validate system requirements. This process is needed in reducing software errors at the early stage of the development of software. The importance of each of the steps in Requirements Engineering is clearly explained in the context of using detailed problem statement from client/customer to get an overview of an existing system along with expectations from the new system. This paper elicits inadequate Requirements Engineering principle as the major cause of poor software development in developing nations using a case study of final year computer science students of a tertiary-education institution in Nigeria.
Abstract: This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.
Abstract: In this paper, the design problem of state estimator for
neural networks with the mixed time-varying delays are investigated
by constructing appropriate Lyapunov-Krasovskii functionals and
using some effective mathematical techniques. In order to derive
several conditions to guarantee the estimation error systems to be
globally exponential stable, we transform the considered systems
into the neural-type time-delay systems. Then with a set of linear
inequalities(LMIs), we can obtain the stable criteria. Finally, three
numerical examples are given to show the effectiveness and less
conservatism of the proposed criterion.
Abstract: The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
Abstract: This paper proposes a complementary combination scheme of affine projection algorithm (APA) filters with different order of input regressors. A convex combination provides an interesting way to keep the advantage of APA having different order of input regressors. Consequently, a novel APA which has the rapid convergence and the reduced steady-state error is derived. Experimental results show the good properties of the proposed algorithm.