Abstract: The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.
Abstract: The polymer foil used for manufacturing of
laminated glass members behaves in a viscoelastic manner with
temperature dependance. This contribution aims at incorporating
the time/temperature-dependent behavior of interlayer to our earlier
elastic finite element model for laminated glass beams. The model
is based on a refined beam theory: each layer behaves according
to the finite-strain shear deformable formulation by Reissner and
the adjacent layers are connected via the Lagrange multipliers
ensuring the inter-layer compatibility of a laminated unit. The
time/temperature-dependent behavior of the interlayer is accounted
for by the generalized Maxwell model and by the time-temperature
superposition principle due to the Williams, Landel, and Ferry.
The resulting system is solved by the Newton method with
consistent linearization and the viscoelastic response is determined
incrementally by the exponential algorithm. By comparing the model
predictions against available experimental data, we demonstrate that
the proposed formulation is reliable and accurately reproduces the
behavior of the laminated glass units.
Abstract: The exponential growth of social media arouses much
attention on public opinion information. The online forums, blogs,
micro blogs are proving to be extremely valuable resources and are
having bulk volume of information. However, most of the social
media data is unstructured and semi structured form. So that it is
more difficult to decipher automatically. Therefore, it is very much
essential to understand and analyze those data for making a right
decision. The online forums hotspot detection is a promising research
field in the web mining and it guides to motivate the user to take right
decision in right time. The proposed system consist of a novel
approach to detect a hotspot forum for any given time period. It uses
aging theory to find the hot terms and E-K-means for detecting the
hotspot forum. Experimental results demonstrate that the proposed
approach outperforms k-means for detecting the hotspot forums with
the improved accuracy.
Abstract: In the past few years, the amount of malicious software
increased exponentially and, therefore, machine learning algorithms
became instrumental in identifying clean and malware files through
(semi)-automated classification. When working with very large
datasets, the major challenge is to reach both a very high malware
detection rate and a very low false positive rate. Another challenge
is to minimize the time needed for the machine learning algorithm to
do so. This paper presents a comparative study between different
machine learning techniques such as linear classifiers, ensembles,
decision trees or various hybrids thereof. The training dataset consists
of approximately 2 million clean files and 200.000 infected files,
which is a realistic quantitative mixture. The paper investigates the
above mentioned methods with respect to both their performance
(detection rate and false positive rate) and their practicability.
Abstract: In this paper, the problem of steady laminar boundary
layer flow and heat transfer over a permeable exponentially
stretching/shrinking sheet with generalized slip velocity is
considered. The similarity transformations are used to transform the
governing nonlinear partial differential equations to a system of
nonlinear ordinary differential equations. The transformed equations
are then solved numerically using the bvp4c function in MATLAB.
Dual solutions are found for a certain range of the suction and
stretching/shrinking parameters. The effects of the suction parameter,
stretching/shrinking parameter, velocity slip parameter, critical shear
rate and Prandtl number on the skin friction and heat transfer
coefficients as well as the velocity and temperature profiles are
presented and discussed.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: This paper presents the simulation results of the
effects of sampling frequency on the total harmonic distortion (THD)
of three-phase inverters using the space vector pulse width
modulation (SVPWM) and space vector control (SVC) algorithms.
The relationship between the variables was studied using curve fitting
techniques, and it has been shown that, for 50 Hz inverters, there is
an exponential relation between the sampling frequency and THD up
to around 8500 Hz, beyond which the performance of the model
becomes irregular, and there is an negative exponential relation
between the sampling frequency and the marginal improvement to
the THD. It has also been found that the performance of SVPWM is
better than that of SVC with the same sampling frequency in most
frequency range, including the range where the performance of the
former is irregular.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: An analysis is carried out to investigate the effect of
magnetic field and heat source on the steady boundary layer flow and
heat transfer of a Casson nanofluid over a vertical cylinder stretching
exponentially along its radial direction. Using a similarity
transformation, the governing mathematical equations, with the
boundary conditions are reduced to a system of coupled, non –linear
ordinary differential equations. The resulting system is solved
numerically by the fourth order Runge – Kutta scheme with shooting
technique. The influence of various physical parameters such as
Reynolds number, Prandtl number, magnetic field, Brownian motion
parameter, thermophoresis parameter, Lewis number and the natural
convection parameter are presented graphically and discussed for non
– dimensional velocity, temperature and nanoparticle volume
fraction. Numerical data for the skin – friction coefficient, local
Nusselt number and the local Sherwood number have been tabulated
for various parametric conditions. It is found that the local Nusselt
number is a decreasing function of Brownian motion parameter Nb
and the thermophoresis parameter Nt.
Abstract: In this paper, we study the rainfall using a time series
for weather stations in Nakhon Ratchasima province in Thailand by
various statistical methods to enable us to analyse the behaviour of
rainfall in the study areas. Time-series analysis is an important tool in
modelling and forecasting rainfall. The ARIMA and Holt-Winter
models were built on the basis of exponential smoothing. All the
models proved to be adequate. Therefore it is possible to give
information that can help decision makers establish strategies for the
proper planning of agriculture, drainage systems and other water
resource applications in Nakhon Ratchasima province. We obtained
the best performance from forecasting with the ARIMA
Model(1,0,1)(1,0,1)12.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: The growth of wireless devices affects the availability
of limited frequencies or spectrum bands as it has been known that
spectrum bands are a natural resource that cannot be added.
Meanwhile, the licensed frequencies are idle most of the time.
Cognitive radio is one of the solutions to solve those problems.
Cognitive radio is a promising technology that allows the unlicensed
users known as secondary users (SUs) to access licensed bands
without making interference to licensed users or primary users (PUs).
As cloud computing has become popular in recent years, cognitive
radio networks (CRNs) can be integrated with cloud platform. One of
the important issues in CRNs is security. It becomes a problem since
CRNs use radio frequencies as a medium for transmitting and CRNs
share the same issues with wireless communication systems. Another
critical issue in CRNs is performance. Security has adverse effect to
performance and there are trade-offs between them. The goal of this
paper is to investigate the performance related to security trade-off in
CRNs with supporting cloud platforms. Furthermore, Queuing
Network Models with preemptive resume and preemptive repeat
identical priority are applied in this project to measure the impact of
security to performance in CRNs with or without cloud platform. The
generalized exponential (GE) type distribution is used to reflect the
bursty inter-arrival and service times at the servers. The results show
that the best performance is obtained when security is disabled and
cloud platform is enabled.
Abstract: A theoretical study has been presented to describe the boundary layer flow and heat transfer on an exponentially shrinking sheet with a variable wall temperature and suction, in the presence of magnetic field. The governing nonlinear partial differential equations are converted into ordinary differential equations by similarity transformation, which are then solved numerically using the shooting method. Results for the skin friction coefficient, local Nusselt number, velocity profiles as well as temperature profiles are presented through graphs and tables for several sets of values of the parameters. The effects of the governing parameters on the flow and heat transfer characteristics are thoroughly examined.
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: Information technology is changing rapidly and the users’ expectations are also growing. Dealing with these changes in information technology, while satisfying the users’ needs and expectations is a big challenge. IT managers need to explore new mechanisms/strategies to enable them to cope with such challenges.
The objectives of this research are to identify the significant challenges that might face IT managers in higher education institutes in the face of the high and ever growing customer expectations and to propose possible solutions to cope with such high-speed changes in information technology.
To achieve these objectives, interviews with the IT professionals from different higher education institutes in Oman were conducted. In addition, documentation (printed and online) related to these institutions were studied and an intensive literature review of published work was examined.
The findings of this research are expected to give a better understanding of the challenges that might face the IT managers at higher education institutes. This acquired understanding is expected to highlight the importance of being adaptable and fast in keeping up with the ever-growing technological changes. Moreover, adopting different tools and technologies could assist IT managers in developing their organisations’ IT policies and strategies.
Abstract: This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: Digital information is expanding in exponential order in our life. Information that is residing online and offline are stored in huge repositories relating to every aspect of our lives. Getting the required information is a task of retrieval systems. Content based image retrieval (CBIR) is a retrieval system that retrieves the required information from repositories on the basis of the contents of the image. Time is a critical factor in retrieval system and using indexed views with CBIR system improves the time efficiency of retrieved results.
Abstract: Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts.
Abstract: We offer a new technique for research of stability of current sheaths in space plasma taking into account the effect of polarization. At the beginning, the found perturbation of the distribution function is used for calculation of the dielectric permeability tensor, which simulates inhomogeneous medium of a current sheath. Further, we in the usual manner solve the system of Maxwell's equations closed with the material equation. The amplitudes of Fourier perturbations are considered to be exponentially decaying through the current sheath thickness. The dispersion equation follows from the nontrivial solution requirement for perturbations of the electromagnetic field. The resulting dispersion equation allows one to study the temporal and spatial characteristics of instability modes of the current sheath (within the limits of the proposed model) over a wide frequency range, including low frequencies.