Abstract: In this paper, in order to categorize ORL database face
pictures, principle Component Analysis (PCA) and Kernel Principal
Component Analysis (KPCA) methods by using Elman neural
network and Support Vector Machine (SVM) categorization methods
are used. Elman network as a recurrent neural network is proposed
for modeling storage systems and also it is used for reviewing the
effect of using PCA numbers on system categorization precision rate
and database pictures categorization time. Categorization stages are
conducted with various components numbers and the obtained results
of both Elman neural network categorization and support vector
machine are compared. In optimum manner 97.41% recognition
accuracy is obtained.
Abstract: Hazardous Material transportation by road is coupled
with inherent risk of accidents causing loss of lives, grievous injuries,
property losses and environmental damages. The most common type
of hazmat road accident happens to be the releases (78%) of
hazardous substances, followed by fires (28%), explosions (14%) and
vapour/ gas clouds (6 %.).
The paper is discussing initially the probable 'Impact Zones'
likely to be caused by one flammable (LPG) and one toxic (ethylene
oxide) chemicals being transported through a sizable segment of a
State Highway connecting three notified Industrial zones in Surat
district in Western India housing 26 MAH industrial units. Three
'hotspots' were identified along the highway segment depending on
the particular chemical traffic and the population distribution within
500 meters on either sides. The thermal radiation and explosion
overpressure have been calculated for LPG / Ethylene Oxide BLEVE
scenarios along with toxic release scenario for ethylene oxide.
Besides, the dispersion calculations for ethylene oxide toxic release
have been made for each 'hotspot' location and the impact zones
have been mapped for the LOC concentrations. Subsequently, the
maximum Initial Isolation and the protective zones were calculated
based on ERPG-3 and ERPG-2 values of ethylene oxide respectively
which are estimated taking the worst case scenario under worst
weather conditions. The data analysis will be helpful to the local
administration in capacity building with respect to rescue /
evacuation and medical preparedness and quantitative inputs to
augment the District Offsite Emergency Plan document.
Abstract: Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.
Abstract: In present article the model of Blended Learning, its advantage at foreign language teaching, and also some problems that can arise during its use are considered. The Blended Learning is a special organization of learning, which allows to combine classroom work and modern technologies in electronic distance teaching environment. Nowadays a lot of European educational institutions and companies use such technology. Through this method: student gets the opportunity to learn in a group (classroom) with a teacher and additionally at home at a convenient time; student himself sets the optimal speed and intensity of the learning process; this method helps student to discipline himself and learn to work independently.
Abstract: Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: Several models have been introduced so far for single
electron box, SEB, which all of them were restricted to DC response
and or low temperature limit. In this paper we introduce a new time
dependent, high temperature analytical model for SEB for the first
time. DC behavior of the introduced model will be verified against
SIMON software and its time behavior will be verified against a
newly published paper regarding step response of SEB.
Abstract: In this paper, a methodology of a model based on
predicting the tool forces oblique machining are introduced by
adopting the orthogonal technique. The applied analytical calculation
is mostly based on Devries model and some parts of the methodology
are employed from Amareggo-Brown model. Model validation is
performed by comparing experimental data with the prediction results
on machining titanium alloy (Ti-6Al-4V) based on micro-cutting tool
perspective. Good agreements with the experiments are observed. A
detailed friction form that affected the tool forces also been examined
with reasonable results obtained.
Abstract: The paper is concerned with relationships between
SSME and ICTs and focuses on the role of Web 2.0 tools in
the service development process. The research presented aims at
exploring how collaborative technologies can support and improve
service processes, highlighting customer centrality and value coproduction.
The core idea of the paper is the centrality of user
participation and the collaborative technologies as enabling factors;
Wikipedia is analyzed as an example. The result of such analysis is
the identification and description of a pattern characterising specific
services in which users collaborate by means of web tools with value
co-producers during the service process. The pattern of collaborative
co-production concerning several categories of services including
knowledge based services is then discussed.
Abstract: Moisture is an important consideration in many
aspects ranging from irrigation, soil chemistry, golf course, corrosion
and erosion, road conditions, weather predictions, livestock feed
moisture levels, water seepage etc. Vegetation and crops always
depend more on the moisture available at the root level than on
precipitation occurrence. In this paper, design of an instrument is
discussed which tells about the variation in the moisture contents of
soil. This is done by measuring the amount of water content in soil by
finding the variation in capacitance of soil with the help of a
capacitive sensor. The greatest advantage of soil moisture sensor is
reduced water consumption. The sensor is also be used to set lower
and upper threshold to maintain optimum soil moisture saturation and
minimize water wilting, contributes to deeper plant root growth
,reduced soil run off /leaching and less favorable condition for insects
and fungal diseases. Capacitance method is preferred because, it
provides absolute amount of water content and also measures water
content at any depth.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: Shear-layer instabilities of a pulsed stack-issued
transverse jet were studied experimentally in a wind tunnel. Jet
pulsations were induced by means of acoustic excitation. Streak
pictures of the smoke-flow patterns illuminated by the laser-light sheet
in the median plane were recorded with a high-speed digital camera.
Instantaneous velocities of the shear-layer instabilities in the flow were
digitized by a hot-wire anemometer. By analyzing the streak pictures
of the smoke-flow visualization, three characteristic flow modes,
synchronized flapping jet, transition, and synchronized shear-layer
vortices, are identified in the shear layer of the pulsed stack-issued
transverse jet at various excitation Strouhal numbers. The shear-layer
instabilities of the pulsed stack-issued transverse jet are synchronized
by acoustic excitation except for transition mode. In transition flow
mode, the shear-layer vortices would exhibit a frequency that would be
twice as great as the acoustic excitation frequency.
Abstract: In research on natural ventilation, and passive cooling
with forced convection, is essential to know how heat flows in a solid
object and the pattern of temperature distribution on their surfaces,
and eventually how air flows through and convects heat from the
surfaces of steel under roof. This paper presents some results from
running the computational fluid dynamic program (CFD) by
comparison between natural ventilation and forced convection within
roof attic that is received directly from solar radiation. The CFD
program for modeling air flow inside roof attic has been modified to
allow as two cases. First case, the analysis under natural ventilation,
is closed area in roof attic and second case, the analysis under forced
convection, is opened area in roof attic. These extend of all cases to
available predictions of variations such as temperature, pressure, and
mass flow rate distributions in each case within roof attic. The
comparison shows that this CFD program is an effective model for
predicting air flow of temperature and heat transfer coefficient
distribution within roof attic. The result shows that forced convection
can help to reduce heat transfer through roof attic and an around area
of steel core has temperature inner zone lower than natural
ventilation type. The different temperature on the steel core of roof
attic of two cases was 10-15 oK.
Abstract: Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.
Abstract: There is increasing evidence that earthquakes produce electromagnetic signals observable at the surface in the extremely low to very low freqency (ELF - VLF) range often in advance to the main event. These precursors are candidates for prediction purposes. Laboratory experiments con´¼ürm that material under load emits an electromagnetic signature, the detailed generation mechanisms how- ever are not well understood yet.
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: The fluid mechanics principle is used extensively in
designing axial flow fans and their associated equipment. This paper presents a computational fluid dynamics (CFD) modeling of air flow
distribution from a radiator axial flow fan used in an acid pump truck Tier4 (APT T4) Repower. This axial flow fan augments the transfer
of heat from the engine mounted on the APT T4.
CFD analysis was performed for an area weighted average static pressure difference at the inlet and outlet of the fan. Pressure contours, velocity vectors, and path lines were plotted for detailing
the flow characteristics for different orientations of the fan blade. The results were then compared and verified against known theoretical observations and actual experimental data. This study
shows that a CFD simulation can be very useful for predicting and understanding the flow distribution from a radiator fan for further
research work.
Abstract: Internet today has a huge impact on all aspects of life,
and also in the area of the broader context of democracy, politics and
politicians. If democracy is freedom of choice, there are a number of
conditions that can ensure in practice the freedom to be achieved and
realized. These preconditions must be achieved regardless of the
manner of voting. The key contribution of ICT to achieve freedom of
choice is that technology enables the correlation of the citizens and
elected representatives on the better way than it was possible without
the Internet. In this sense, we can say that the Internet and ICT are
changing significantly, and potentially improving the environment in
which democratic processes are taking place. This paper aims to
describe trends in use of ICT in democratic processes, and analyzes
the challenges for implementation of e-Democracy in Montenegro
Abstract: Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.