Abstract: In a pilot plant scale of a fluidized bed reactor, a
reduction reaction of sodium sulfate by natural gas has been
investigated. Natural gas is applied in this study as a reductant. Feed
density, feed mass flow rate, natural gas and air flow rate
(independent parameters)and temperature of bed and CO
concentration in inlet and outlet of reactor (dependent parameters)
were monitored and recorded at steady state. The residence time was
adjusted close to value of traditional reaction [1]. An artificial neural
network (ANN) was established to study dependency of yield and
carbon gradient on operating parameters. Resultant 97% accuracy of
applied ANN is a good prove that natural gas can be used as a
reducing agent. Predicted ANN model for relation between other
sources carbon gradient (accuracy 74%) indicates there is not a
meaningful relation between other sources carbon variation and
reduction process which means carbon in granule does not have
significant effect on the reaction yield.
Abstract: Developments in communication technologies
especially in wireless have enabled the progress of low-cost and lowpower
wireless sensor networks (WSNs). The features of such WSN
are holding minimal energy, weak computational capabilities,
wireless communication and an open-medium nature where sensors
are deployed. WSN is underpinned by application driven such as
military applications, the health sector, etc. Due to the intrinsic nature
of the network and application scenario, WSNs are vulnerable to
many attacks externally and internally. In this paper we have focused
on the types of internal attacks of WSNs based on OSI model and
discussed some security requirements, characterizers and challenges
of WSNs, by which to contribute to the WSN-s security research.
Abstract: Tourism researchers have recently focused on repeat visitation as a part of destination loyalty. Different models have also considered satisfaction as the main determinant of revisit intention, while findings in many studies show it as a continuous issue. This conceptual paper attempts at evaluating recent empirical studies on satisfaction and revisit intention. Based on limitations and gaps in recent studies, the current paper suggests a new model that would be more comprehensive than those in previous studies. The new model offers new relationships between antecedents (destination image, perceived value, specific novelty seeking, and distance to destination) and both of satisfaction and revisit intention. Revisit intention in turn is suggested to be measured in a temporal approach.
Abstract: This paper presents the results of corrosion fatigue
crack growth behaviour of a Ni-Cr-Mn steel commonly used in
marine applications. The effect of mechanical variables such as
frequency and load ratio on fatigue crack growth rate at various
stages has been studied using compact tension (C(T)) specimens
along the rolling direction of steel plate under 3.5% saturated NaCl
aqueous environment. The significance of crack closure on corrosion
fatigue, and the validity of Elber-s empirical linear crack closure
model with the ASTM compliance offset method have been
examined.
Fatigue crack growth rate is higher and threshold stress intensities
are lower in aqueous environment compared to the lab air conditions.
It is also observed that the crack growth rate increases at lower
frequencies. The higher stress ratio promotes the crack growth. The
effect of oxidization and corrosion pit formation is very less as the
stress ratio is increased. It is observed that as stress ratios are
increased, the Elber-s crack closure model agrees well with the crack
closure estimated by the ASTM compliance offset method for tests
conducted at 5Hz frequency compared to tests conducted at 1Hz in
corrosive environment.
Abstract: Matching algorithms have significant importance in
speaker recognition. Feature vectors of the unknown utterance are
compared to feature vectors of the modeled speakers as a last step in
speaker recognition. A similarity score is found for every model in
the speaker database. Depending on the type of speaker recognition,
these scores are used to determine the author of unknown speech
samples. For speaker verification, similarity score is tested against a
predefined threshold and either acceptance or rejection result is
obtained. In the case of speaker identification, the result depends on
whether the identification is open set or closed set. In closed set
identification, the model that yields the best similarity score is
accepted. In open set identification, the best score is tested against a
threshold, so there is one more possible output satisfying the
condition that the speaker is not one of the registered speakers in
existing database. This paper focuses on closed set speaker
identification using a modified version of a well known matching
algorithm. The results of new matching algorithm indicated better
performance on YOHO international speaker recognition database.
Abstract: In this paper, the sum of squares in linear regression is
reduced to sum of squares in semi-parametric regression. We
indicated that different sums of squares in the linear regression are
similar to various deviance statements in semi-parametric regression.
In addition to, coefficient of the determination derived in linear
regression model is easily generalized to coefficient of the
determination of the semi-parametric regression model. Then, it is
made an application in order to support the theory of the linear
regression and semi-parametric regression. In this way, study is
supported with a simulated data example.
Abstract: Young patients suffering from Cerebral Palsy are
facing difficult choices concerning heavy surgeries. Diagnosis settled
by surgeons can be complex and on the other hand decision for
patient about getting or not such a surgery involves important
reflection effort. Proposed software combining prediction for
surgeries and post surgery kinematic values, and from 3D model
representing the patient is an innovative tool helpful for both patients
and medicine professionals. Beginning with analysis and
classification of kinematics values from Data Base extracted from
gait analysis in 3 separated clusters, it is possible to determine close
similarity between patients. Prediction surgery best adapted to
improve a patient gait is then determined by operating a suitable
preconditioned neural network. Finally, patient 3D modeling based
on kinematic values analysis, is animated thanks to post surgery
kinematic vectors characterizing the closest patient selected from
patients clustering.
Abstract: A numerical simulation of vortex-induced vibration of
a 2-dimensional elastic circular cylinder with two degree of freedom
under the uniform flow is calculated when Reynolds is 200.
2-dimensional incompressible Navier-Stokes equations are solved
with the space-time finite element method, the equation of the cylinder
motion is solved with the new explicit integral method and the mesh
renew is achieved by the spring moving mesh technology. Considering
vortex-induced vibration with the low reduced damping parameter, the
variety trends of the lift coefficient, the drag coefficient, the
displacement of cylinder are analyzed under different oscillating
frequencies of cylinder. The phenomena of locked-in, beat and
phases-witch were captured successfully. The evolution of vortex
shedding from the cylinder with time is discussed. There are very
similar trends in characteristics between the results of the one degree
of freedom cylinder model and that of the two degree of freedom
cylinder model. The streamwise vibrations have a certain effect on the
lateral vibrations and their characteristics.
Abstract: Public housing is a vital factor in community
development. Successful city, housing and eco system regeneration
design is essential in providing positive community development.
This concerns work places, nice dwellings, providing premises for
child care, care of the elderly, providing qualitative premises for
different kinds of commercial service, providing a nice built
environment and housing areas and not the least activating tenants.
The public housing companies give value to society by stimulating
people, renovating socially and economically sustainable as well as
being partners to local business and authorities. By their activities the
housing companies contribute to sustainable local and regional
growth and the identity and reputation of cities. A Social, Economic
and Ecological Reputation Effect (SEERE) model for actions to
promote housing and community reputation is presented. The model
emphasizes regenerative actions to restore natural eco systems as part
of housing renewal strategies and to strengthen municipality
reputation.
Abstract: In this paper, a Gaussian multiple input multiple output multiple eavesdropper (MIMOME) channel is considered where a transmitter communicates to a receiver in the presence of an eavesdropper. We present a technique for determining the secrecy capacity of the multiple input multiple output (MIMO) channel under Gaussian noise. We transform the degraded MIMOME channel into multiple single input multiple output (SIMO) Gaussian wire-tap channels and then use scalar approach to convert it into two equivalent multiple input single output (MISO) channels. The secrecy capacity model is then developed for the condition where the channel state information (CSI) for main channel only is known to the transmitter. The results show that the secret communication is possible when the eavesdropper channel noise is greater than a cutoff noise level. The outage probability is also analyzed of secrecy capacity is also analyzed. The effect of fading and outage probability is also analyzed.
Abstract: Segmentation of a color image composed of different
kinds of regions can be a hard problem, namely to compute for an
exact texture fields. The decision of the optimum number of
segmentation areas in an image when it contains similar and/or un
stationary texture fields. A novel neighborhood-based segmentation
approach is proposed. A genetic algorithm is used in the proposed
segment-pass optimization process. In this pass, an energy function,
which is defined based on Markov Random Fields, is minimized. In
this paper we use an adaptive threshold estimation method for image
thresholding in the wavelet domain based on the generalized
Gaussian distribution (GGD) modeling of sub band coefficients. This
method called Normal Shrink is computationally more efficient and
adaptive because the parameters required for estimating the threshold
depend on sub band data energy that used in the pre-stage of
segmentation. A quad tree is employed to implement the multi
resolution framework, which enables the use of different strategies at
different resolution levels, and hence, the computation can be
accelerated. The experimental results using the proposed
segmentation approach are very encouraging.
Abstract: With the rapid advanced of technology, the industrial processes become increasingly demanding, from the point of view, power quality and controllability. The advent of multi levels inverters responds partially to these requirements. But actually, the new generation of multi-cells inverters permits to reach more performances, since, it offers more voltage levels. The disadvantage in the increase of voltage levels by the number of cells in cascades is on account of series igbts synchronisation loss, from where, a limitation of cells in cascade to 4. Regarding to these constraints, a new topology is proposed in this paper, which increases the voltage levels of the three-cell inverter from 4 to 8; with the same number of igbts, and using less stored energy in the flaying capacitors. The details of operation and modelling of this new inverter structure are also presented, then tested thanks to a three phase induction motor. KeywordsFlaying capacitors, Multi-cells inverter, pwm, switchers, modelling.
Abstract: The present work deals with the structural analysis of
turbine blades and modeling of turbine blades. A common failure
mode for turbine machines is high cycle of fatigue of compressor and
turbine blades due to high dynamic stresses caused by blade vibration
and resonance within the operation range of the machinery. In this
work, proper damping system will be analyzed to reduce the
vibrating blade. The main focus of the work is the modeling of under
platform damper to evaluate the dynamic analysis of turbine-blade
vibrations. The system is analyzed using Bond graph technique. Bond
graph is one of the most convenient ways to represent a system from
the physical aspect in foreground. It has advantage of putting together
multi-energy domains of a system in a single representation in a
unified manner. The bond graph model of dry friction damper is
simulated on SYMBOLS-shakti® software. In this work, the blades
are modeled as Timoshenko beam. Blade Vibrations under different
working conditions are being analyzed numerically.
Abstract: Small and Medium Sized Enterprises (SMEs) play an important role in many economies. In New Zealand, for example, 97% of all manufacturing companies employ less than 100 staff, and generate the predominant part of this industry sector-s economic output. Manufacturing SMEs as a group also have a significant impact on the environment. This situation is similar in many developed economies, including the European Union. Sustainable economic development therefore needs to strongly consider the role of manufacturing SMEs, who generally find it challenging to move towards more environmentally friendly business practices. This paper presents a systems thinking approach to modelling and understanding the factors which have an influence on the successful uptake of environmental practices in small and medium sized manufacturing companies. It presents a number of causal loop diagrams which have been developed based on primary action research, and a thorough understanding of the literature in this area. The systems thinking model provides the basis for further development of a strategic framework for the successful uptake of environmental innovation in manufacturing SMEs.
Abstract: The hydrologic time series data display periodic
structure and periodic autoregressive process receives considerable
attention in modeling of such series. In this communication long
term record of monthly waste flow of Lyari river is utilized to
quantify by using PAR modeling technique. The parameters of
model are estimated by using Frances & Paap methodology. This
study shows that periodic autoregressive model of order 2 is the most
parsimonious model for assessing periodicity in waste flow of the
river. A careful statistical analysis of residuals of PAR (2) model is
used for establishing goodness of fit. The forecast by using proposed
model confirms significance and effectiveness of the model.
Abstract: A model based fault detection and diagnosis
technique for DC motor is proposed in this paper. Fault detection
using Kalman filter and its different variants are compared. Only
incipient faults are considered for the study. The Kalman Filter
iterations and all the related computations required for fault detection
and fault confirmation are presented. A second order linear state
space model of DC motor is used for this work. A comparative
assessment of the estimates computed from four different observers
and their relative performance is evaluated.
Abstract: Where renewable energy sources, solar, hydro, wind are available the remote communities and businesses can be provided with the most reliable and affordable source of electrical energy. This paper presents a model of safari rest contains all the necessary services for the interested tourists who visit the safari Sinai desert. The PV energy system provides the rural energy needs of remote communities. A photovoltaic renewable energy system is designed to feed the global Ac and Dc electrical required load of this safari rest . The benefits of photovoltaic renewable energy at rural applications are its versatility and convenience. This model of safari rest must be taken in consideration by Egyptian Government as it will provide the tourism plane by new interested tourism field which put a big spot on Red sea area: El Ghordaka.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: As the network based technologies become
omnipresent, demands to secure networks/systems against threat
increase. One of the effective ways to achieve higher security is
through the use of intrusion detection systems (IDS), which are a
software tool to detect anomalous in the computer or network. In this
paper, an IDS has been developed using an improved machine
learning based algorithm, Locally Linear Neuro Fuzzy Model
(LLNF) for classification whereas this model is originally used for
system identification. A key technical challenge in IDS and LLNF
learning is the curse of high dimensionality. Therefore a feature
selection phase is proposed which is applicable to any IDS. While
investigating the use of three feature selection algorithms, in this
model, it is shown that adding feature selection phase reduces
computational complexity of our model. Feature selection algorithms
require the use of a feature goodness measure. The use of both a
linear and a non-linear measure - linear correlation coefficient and
mutual information- is investigated respectively