Abstract: This paper presents a unified approach based graph
theory and system theory postulates for the modeling and analysis
of Simple open cycle Gas turbine system. In the present paper, the
simple open cycle gas turbine system has been modeled up to its subsystem
level and system variables have been identified to develop the
process subgraphs. The theorems and algorithms of the graph theory
have been used to represent behavioural properties of the system like
rate of heat and work transfers rates, pressure drops and temperature
drops in the involved processes of the system. The processes have
been represented as edges of the process subgraphs and their limits
as the vertices of the process subgraphs. The system across variables
and through variables has been used to develop terminal equations of
the process subgraphs of the system. The set of equations developed
for vertices and edges of network graph are used to solve the system
for its process variables.
Abstract: In this study, a software has been developed to predict
the optimum conditions for drying of cotton based yarn bobbins in a
hot air dryer. For this purpose, firstly, a suitable drying model has
been specified using experimental drying behavior for different
values of drying parameters. Drying parameters in the experiments
were drying temperature, drying pressure, and volumetric flow rate of
drying air. After obtaining a suitable drying model, additional curve
fittings have been performed to obtain equations for drying time and
energy consumption taking into account the effects of drying
parameters. Then, a software has been developed using Visual Basic
programming language to predict the optimum drying conditions for
drying time and energy consumption.
Abstract: In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.
Abstract: Despite the fact that B2c eCommerce has become
important in numerous economies, its adoption varies from country to
country. This paper aims to identify the factors affecting (enabling or
inhibiting) B2c eCommerce and to determine their quantitative
impact on the diffusion of online sales across countries. A dynamic
panel model analyzing the relationship between 13 factors
(Macroeconomic, Demographic, Socio-Cultural, Infrastructural and
Offer related) stemming from a complete literature analysis and the
B2c eCommerce value in 45 countries over 9 years has been
developed. Having a positive correlation coefficient, GDP, mobile
penetration, Internet user penetration and credit card penetration
resulted as enabling drivers of the B2c eCommerce value across
countries, whereas, having a negative correlation coefficient,equal
distribution of income and the development of traditional retailing
network act as inhibiting factors.
Abstract: In this paper, we study the stability of a fractional order delayed predator-prey model. By using the Laplace transform, we introduce a characteristic equation for the above system. It is shown that if all roots of the characteristic equation have negative parts, then the equilibrium of the above fractional order predator-prey system is Lyapunov globally asymptotical stable. An example is given to show the effectiveness of the approach presented in this paper.
Abstract: Although lots of experiments have been done in enhanced oil recovery, the number of experiments which consider the effects of local and global heterogeneity on efficiency of enhanced oil recovery based on the polymer-surfactant flooding is low and rarely done. In this research, we have done numerous experiments of water flooding and polymer-surfactant flooding on a five spot glass micromodel in different conditions such as different positions of layers. In these experiments, five different micromodels with three different pore structures are designed. Three models with different layer orientation, one homogenous model and one heterogeneous model are designed. In order to import the effect of heterogeneity of porous media, three types of pore structures are distributed accidentally and with equal ratio throughout heterogeneous micromodel network according to random normal distribution. The results show that maximum EOR recovery factor will happen in a situation where the layers are orthogonal to the path of mainstream and the minimum EOR recovery factor will happen in a situation where the model is heterogeneous. This experiments show that in polymer-surfactant flooding, with increase of angles of layers the EOR recovery factor will increase and this recovery factor is strongly affected by local heterogeneity around the injection zone.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: In the last few years, three multivariate spectral
analysis techniques namely, Principal Component Analysis (PCA),
Independent Component Analysis (ICA) and Non-negative Matrix
Factorization (NMF) have emerged as effective tools for oscillation
detection and isolation. While the first method is used in determining
the number of oscillatory sources, the latter two methods
are used to identify source signatures by formulating the detection
problem as a source identification problem in the spectral domain.
In this paper, we present a critical drawback of the underlying linear
(mixing) model which strongly limits the ability of the associated
source separation methods to determine the number of sources
and/or identify the physical source signatures. It is shown that the
assumed mixing model is only valid if each unit of the process gives
equal weighting (all-pass filter) to all oscillatory components in its
inputs. This is in contrast to the fact that each unit, in general, acts
as a filter with non-uniform frequency response. Thus, the model
can only facilitate correct identification of a source with a single
frequency component, which is again unrealistic. To overcome
this deficiency, an iterative post-processing algorithm that correctly
identifies the physical source(s) is developed. An additional issue
with the existing methods is that they lack a procedure to pre-screen
non-oscillatory/noisy measurements which obscure the identification
of oscillatory sources. In this regard, a pre-screening procedure
is prescribed based on the notion of sparseness index to eliminate
the noisy and non-oscillatory measurements from the data set used
for analysis.
Abstract: Knowing the geometrical object pose of products in manufacturing line before robot manipulation is required and less time consuming for overall shape measurement. In order to perform it, the information of shape representation and matching of objects is become required. Objects are compared with its descriptor that conceptually subtracted from each other to form scalar metric. When the metric value is smaller, the object is considered closed to each other. Rotating the object from static pose in some direction introduce the change of value in scalar metric value of boundary information after feature extraction of related object. In this paper, a proposal method for indexing technique for retrieval of 3D geometrical models based on similarity between boundaries shapes in order to measure 3D CAD object pose using object shape feature matching for Computer Aided Testing (CAT) system in production line is proposed. In experimental results shows the effectiveness of proposed method.
Abstract: The most Malaria cases are occur along Thai-Mynmar border. Mathematical model for the transmission of Plasmodium falciparum and Plasmodium vivax malaria in a mixed population of Thais and migrant Burmese living along the Thai-Myanmar Border is studied. The population is separated into two groups, Thai and Burmese. Each population is divided into susceptible, infected, dormant and recovered subclasses. The loss of immunity by individuals in the infected class causes them to move back into the susceptible class. The person who is infected with Plasmodium vivax and is a member of the dormant class can relapse back into the infected class. A standard dynamical method is used to analyze the behaviors of the model. Two stable equilibrium states, a disease-free state and an epidemic state, are found to be possible in each population. A disease-free equilibrium state in the Thai population occurs when there are no infected Burmese entering the community. When infected Burmese enter the Thai community, an epidemic state can occur. It is found that the disease-free state is stable when the threshold number is less than one. The epidemic state is stable when a second threshold number is greater than one. Numerical simulations are used to confirm the results of our model.
Abstract: To help the client to select a competent agent
construction enterprise (ACE), this study aims to investigate the
selection standards by using the Fuzzy Analytic Hierarchy Process
(FAHP) and build an evaluation mathematical model with Grey
Relational Analysis (GRA). According to the outputs of literature
review, four orderly levels are established within the model, taking the
consideration of various agent construction models in practice. Then,
the process of applying FAHP and GRA is discussed in detailed.
Finally, through a case study, this paper illustrates how to apply these
methods in getting the weights of each standard and the final
assessment result.
Abstract: A dynamic stall-corrected Blade Element-Momentum algorithm based on a hybrid polar is validated through the comparison with Sandia experimental measurements on a 5-m diameter wind turbine of Troposkien shape. Different dynamic stall models are evaluated. The numerical predictions obtained using the extended aerodynamic coefficients provided by both Sheldal and Klimas and Raciti Castelli et al. are compared to experimental data, determining the potential of the hybrid database for the numerical prediction of vertical-axis wind turbine performances.
Abstract: An original DEA model is to evaluate each DMU
optimistically, but the interval DEA Model proposed in this paper
has been formulated to obtain an efficiency interval consisting of
Evaluations from both the optimistic and the pessimistic view points.
DMUs are improved so that their lower bounds become so large as to
attain the maximum Value one. The points obtained by this method
are called ideal points. Ideal PPS is calculated by ideal of efficiency
DMUs. The purpose of this paper is to rank DMUs by this ideal PPS.
Finally we extend the efficiency interval of a DMU under variable
RTS technology.
Abstract: Fast delay estimation methods, as opposed to
simulation techniques, are needed for incremental performance
driven layout synthesis. On-chip inductive effects are becoming
predominant in deep submicron interconnects due to increasing clock
speed and circuit complexity. Inductance causes noise in signal
waveforms, which can adversely affect the performance of the circuit
and signal integrity. Several approaches have been put forward which
consider the inductance for on-chip interconnect modelling. But for
even much higher frequency, of the order of few GHz, the shunt
dielectric lossy component has become comparable to that of other
electrical parameters for high speed VLSI design. In order to cope up
with this effect, on-chip interconnect has to be modelled as
distributed RLCG line. Elmore delay based methods, although
efficient, cannot accurately estimate the delay for RLCG interconnect
line. In this paper, an accurate analytical delay model has been
derived, based on first and second moments of RLCG
interconnection lines. The proposed model considers both the effect
of inductance and conductance matrices. We have performed the
simulation in 0.18μm technology node and an error of as low as less
as 5% has been achieved with the proposed model when compared to
SPICE. The importance of the conductance matrices in interconnect
modelling has also been discussed and it is shown that if G is
neglected for interconnect line modelling, then it will result an delay
error of as high as 6% when compared to SPICE.
Abstract: In the past decade, because of wide applications of
hybrid systems, many researchers have considered modeling and
control of these systems. Since switching systems constitute an
important class of hybrid systems, in this paper a method for optimal
control of linear switching systems is described. The method is also
applied on the two-tank system which is a much appropriate system
to analyze different modeling and control techniques of hybrid
systems. Simulation results show that, in this method, the goals of
control and also problem constraints can be satisfied by an
appropriate selection of cost function.
Abstract: This paper proposes an innovative approach for the Connection Admission Control (CAC) problem. Starting from an abstract network modelling, the CAC problem is formulated in a technology independent fashion allowing the proposed concepts to be applied to any wireless and wired domain. The proposed CAC is decoupled from the other Resource Management procedures, but cooperates with them in order to guarantee the desired QoS requirements. Moreover, it is based on suitable performance measurements which, by using proper predictors, allow to forecast the domain dynamics in the next future. Finally, the proposed CAC control scheme is based on a feedback loop aiming at maximizing a suitable performance index accounting for the domain throughput, whilst respecting a set of constraints accounting for the QoS requirements.