Abstract: The development of the signal compression
algorithms is having compressive progress. These algorithms are
continuously improved by new tools and aim to reduce, an average,
the number of bits necessary to the signal representation by means of
minimizing the reconstruction error. The following article proposes
the compression of Arabic speech signal by a hybrid method
combining the wavelet transform and the linear prediction. The
adopted approach rests, on one hand, on the original signal
decomposition by ways of analysis filters, which is followed by the
compression stage, and on the other hand, on the application of the
order 5, as well as, the compression signal coefficients. The aim of
this approach is the estimation of the predicted error, which will be
coded and transmitted. The decoding operation is then used to
reconstitute the original signal. Thus, the adequate choice of the
bench of filters is useful to the transform in necessary to increase the
compression rate and induce an impercevable distortion from an
auditive point of view.
Abstract: Most people know through experience and intuition what the word „sport“ means. Sport includes a combination of these configurations when it involves team competitions, tournaments, or matches in dual sports or individual sports. Sport management - it is an area of professional endeavor in which a variety of sport-related managerial careers exist and it is also an area of academic professional preparation. Exists three unique aspects of sport management: sport marketing, sport enterprise financial structures and sport industry career paths. The aim of the paper was to highlight the growing importance of sport in contemporary society, especially to emphasize its socio-economic benefits and refer to the development of sport management and marketing. The article has shown that sport contributes 2-3% to gross domestic product in the Czech Republic and that the demand for experts, specialists educated for the sports manager profession is growing.
Abstract: This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: In the traditional theory of non-uniform torsion the
axial displacement field is expressed as the product of the unit twist
angle and the warping function. The first one, variable along the
beam axis, is obtained by a global congruence condition; the second
one, instead, defined over the cross-section, is determined by solving
a Neumann problem associated to the Laplace equation, as well as for
the uniform torsion problem.
So, as in the classical theory the warping function doesn-t punctually
satisfy the first indefinite equilibrium equation, the principal aim of
this work is to develop a new theory for non-uniform torsion of
beams with axial symmetric cross-section, fully restrained on both
ends and loaded by a constant torque, that permits to punctually
satisfy the previous equation, by means of a trigonometric expansion
of the axial displacement and unit twist angle functions.
Furthermore, as the classical theory is generally applied with good
results to the global and local analysis of ship structures, two beams
having the first one an open profile, the second one a closed section,
have been analyzed, in order to compare the two theories.
Abstract: This research focuses on the use of a recommender
system in decision support by means of a used car dealer case study
in Bangkok Metropolitan. The goal is to develop an effective used car
purchasing system for dealers based on the above premise. The
underlying principle rests on content-based recommendation from a
set of usability surveys. A prototype was developed to conduct
buyers- survey selected from 5 experts and 95 general public. The
responses were analyzed to determine the mean and standard
deviation of buyers- preference. The results revealed that both groups
were in favor of using the proposed system to assist their buying
decision. This indicates that the proposed system is meritorious to
used car dealers.
Abstract: A full six degrees of freedom (6-DOF) flight dynamics
model is proposed for the accurate prediction of short and long-range
trajectories of high spin and fin-stabilized projectiles via atmospheric
flight to final impact point. The projectiles is assumed to be both rigid
(non-flexible), and rotationally symmetric about its spin axis launched
at low and high pitch angles. The mathematical model is based on the
full equations of motion set up in the no-roll body reference frame and
is integrated numerically from given initial conditions at the firing
site. The projectiles maneuvering motion depends on the most
significant force and moment variations, in addition to wind and
gravity. The computational flight analysis takes into consideration the
Mach number and total angle of attack effects by means of the
variable aerodynamic coefficients. For the purposes of the present
work, linear interpolation has been applied from the tabulated database
of McCoy-s book. The developed computational method gives
satisfactory agreement with published data of verified experiments and
computational codes on atmospheric projectile trajectory analysis for
various initial firing flight conditions.
Abstract: In this study the effect of incorporation of recycled
glass-fibre reinforced polymer (GFRP) waste materials, obtained by
means of milling processes, on mechanical behaviour of polyester
polymer mortars was assessed. For this purpose, different contents of
recycled GFRP waste powder and fibres, with distinct size gradings,
were incorporated into polyester based mortars as sand aggregates
and filler replacements. Flexural and compressive loading capacities
were evaluated and found better than unmodified polymer mortars.
GFRP modified polyester based mortars also show a less brittle
behaviour, with retention of some loading capacity after peak load.
Obtained results highlight the high potential of recycled GFRP waste
materials as efficient and sustainable reinforcement and admixture for
polymer concrete and mortars composites, constituting an emergent
waste management solution.
Abstract: An area-integrating method that uses the technique of total integrated light scatter for evaluating the root mean square height of the surface Sq has been presented in the paper. It is based on the measurement of the scatter power using a flat photodiode integrator rather than an optical sphere or a hemisphere. By this means, one can obtain much less expensive and smaller instruments than traditional ones. Thanks to this, they could find their application for surface control purposes, particularly in small and medium size enterprises. A description of the functioning of the measuring unit as well as the impact caused by different factors on its properties is presented first. Next, results of measurements of the Sq values performed for optical, silicon and metal samples have been shown. It has been also proven that they are in a good agreement with the results obtained using the Ulbricht sphere instrument.
Abstract: In this paper we study the rheonomic mechanical systems from the point of view of Lagrange geometry, by means of its canonical semispray. We present an example of the constraint motion of a material point, in the rheonomic case.
Abstract: The adsorption of simulated aqueous solution containing textile remazol reactive dye, namely Red 3BS by palm shell activated carbon (PSAC) as adsorbent was carried out using Response Surface Methodology (RSM). A Box-Behnken design in three most important operating variables; initial dye concentration, dosage of adsorbent and speed of impeller was employed for experimental design and optimization of results. The significance of independent variables and their interactions were tested by means of the analysis of variance (ANOVA) with 95% confidence limits. Model indicated that with the increasing of dosage and speed give the result of removal up to 90% with the capacity uptake more than 7 mg/g. High regression coefficient between the variables and the response (R-Sq = 93.9%) showed of good evaluation of experimental data by polynomial regression model.
Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.
Abstract: Temperature dependence of force of gravitation is one
of the fundamental problems of physics. This problem has got special
value in connection with that the general theory of relativity,
supposing the weakest positive influence of a body temperature on its
weight, actually rejects an opportunity of measurement of negative
influence of temperature on gravity in laboratory conditions. Really,
the recognition of negative temperature dependence of gravitation,
for example, means basic impossibility of achievement of a
singularity («a black hole») at a gravitational collapse. Laboratory
experiments with exact weighing the heated up metal samples,
indicating negative influence temperatures of bodies on their physical
weight are described. Influence of mistakes of measurements is
analyzed. Calculations of distribution of temperature in volume of the
bar, agreed with experimental data of time dependence of weight of
samples are executed. The physical substantiation of negative
temperature dependence of weight of the bodies, based on correlation
of acceleration at thermal movement of micro-particles of a body and
its absolute temperature, are given.
Abstract: The paper aims to show that implementing different
types of reflectors in solar energy systems, will dramatically improve
energy production by means of concentrating and intensifying more
sunlight onto a solar cell. The Solar Intensifier unit is designed to
increase efficiency and performance of a set of solar panels. The unit
was fabricated and tested. The experimental results show good
improvement in the performance of the solar energy system.
Abstract: The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means- DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMM-based baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.
Abstract: Initial values of reference vectors have significant influence on recognition accuracy in LVQ. There are several existing techniques, such as SOM and k-means, for setting initial values of reference vectors, each of which has provided some positive results. However, those results are not sufficient for the improvement of recognition accuracy. This study proposes an ACO-used method for initializing reference vectors with an aim to achieve recognition accuracy higher than those obtained through conventional methods. Moreover, we will demonstrate the effectiveness of the proposed method by applying it to the wine data and English vowel data and comparing its results with those of conventional methods.
Abstract: Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EOS) is a modified SAFT EOS with three pure component specific parameters: segment number (m), diameter (σ) and energy (ε). These PC-SAFT parameters need to be determined for each component under the conditions of interest by fitting experimental data, such as vapor pressure, density or heat capacity. PC-SAFT parameters for propane, ethylene and hydrogen in supercritical region were successfully estimated by fitting experimental density data available in literature. The regressed PCSAFT parameters were compared with the literature values by means of estimating pure component density and calculating average absolute deviation between the estimated and experimental density values. PC-SAFT parameters available in literature especially for ethylene and hydrogen estimated density in supercritical region reasonably well. However, the regressed PC-SAFT parameters performed better in supercritical region than the PC-SAFT parameters from literature.
Abstract: The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
Abstract: Three alumina-supported Pt-Sn catalysts have been
prepared by means of co-impregnation and characterized by XRD and
N2 adsorption. The influence of catalyst composition and reaction
conditions on the conversion and selectivity were investigated in the
hydrogenation of acetic acid in an isothermal integral fixed bed
reactor. The experiments were performed on the temperature interval
468-548 K, liquid hourly space velocity (LHSV) of 0.3-0.7h-1,
pressures between 1.0 and 5.0Mpa. A good compromise of
0.75%Pt-1.5%Sn can act as an optimized acetic acid hydrogenation
catalyst, and the conversion and selectivity can be tuned through the
variation of reaction conditions.
Abstract: Recently studies in area of supply chain network
(SCN) have focused on the disruption issues in distribution systems.
Also this paper extends the previous literature by providing a new biobjective
model for cost minimization of designing a three echelon
SCN across normal and failure scenarios with considering multi
capacity option for manufacturers and distribution centers. Moreover,
in order to solve the problem by means of LINGO software, novel
model will be reformulated through a branch of LP-Metric method
called Min-Max approach.