Abstract: Movable power sources of proton exchange
membrane fuel cells (PEMFC) are the important research done in the
current fuel cells (FC) field. The PEMFC system control influences
the cell performance greatly and it is a control system for industrial
complex problems, due to the imprecision, uncertainty and partial
truth and intrinsic nonlinear characteristics of PEMFCs. In this paper
an adaptive PI control strategy using neural network adaptive Morlet
wavelet for control is proposed. It is based on a single layer feed
forward neural networks with hidden nodes of adaptive morlet
wavelet functions controller and an infinite impulse response (IIR)
recurrent structure. The IIR is combined by cascading to the network
to provide double local structure resulting in improving speed of
learning. The proposed method is applied to a typical 1 KW PEMFC
system and the results show the proposed method has more accuracy
against to MLP (Multi Layer Perceptron) method.
Abstract: LabVIEW and SIMULINK are two most widely used
graphical programming environments for designing digital signal
processing and control systems. Unlike conventional text-based
programming languages such as C, Cµ and MATLAB, graphical
programming involves block-based code developments, allowing a
more efficient mechanism to build and analyze control systems. In
this paper a LabVIEW environment has been employed as a
graphical user interface for monitoring the operation of a controlled
distillation column, by visualizing both the closed loop performance
and the user selected control conditions, while the column dynamics
has been modeled under the SIMULINK environment. This tool has
been applied to the PID based decoupled control of a binary
distillation column. By means of such integrated environments the
control designer is able to monitor and control the plant behavior and
optimize the response when both, the quality improvement of
distillation products and the operation efficiency tasks, are
considered.
Abstract: Although many researchers have studied the flow
hydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different
methods have been presented for these channels but extending them
for all types of compound channels with different geometrical and
hydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating
curves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed
slope, main channel side slopes, flood plains side slopes and berm
inclination and one output variable (flow discharge), have been used
in ANNs. Comparison of ANNs model and traditional method
(divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and
relative roughness have 19.3 and 12.2 percent of importance, respectively. On the other hand, shape parameter, main channel and
flood plains side slopes with 2.1, 3.8 and 3.8 percent of contribution, have the least importance.
Abstract: Set covering problem is a classical problem in
computer science and complexity theory. It has many applications,
such as airline crew scheduling problem, facilities location problem,
vehicle routing, assignment problem, etc. In this paper, three
different techniques are applied to solve set covering problem.
Firstly, a mathematical model of set covering problem is introduced
and solved by using optimization solver, LINGO. Secondly, the
Genetic Algorithm Toolbox available in MATLAB is used to solve
set covering problem. And lastly, an ant colony optimization method
is programmed in MATLAB programming language. Results
obtained from these methods are presented in tables. In order to
assess the performance of the techniques used in this project, the
benchmark problems available in open literature are used.
Abstract: The mathematical modeling of different biological
processes is usually used to predict or assess behavior of systems in
which these processes take place. This study deals with mathematical
and computer modeling of bi-substrate enzymatic reactions with
ping-pong mechanism, which play an important role in different
biochemical pathways. Besides that, three models of competitive
inhibition were designed using different software packages. The main
objective of this study is to represent the results from in silico
investigation of bi-substrate enzymatic reactions with ordered pingpong
mechanism in the presence of competitive inhibitors, as well as
to describe in details the inhibition effects. The simulation of the
models with certain kinetic parameters allowed investigating the
behavior of reactions as well as determined some interesting aspects
concerning influence of different cases of competitive inhibition.
Simultaneous presence of two inhibitors, competitive to the S1 and S2
substrates have been studied. Moreover, we have found the pattern of
simultaneous influence of both inhibitors.
Abstract: There exists an injective, information-preserving function
that maps a semantic network (i.e a directed labeled network)
to a directed network (i.e. a directed unlabeled network). The edge
label in the semantic network is represented as a topological feature
of the directed network. Also, there exists an injective function that
maps a directed network to an undirected network (i.e. an undirected
unlabeled network). The edge directionality in the directed network
is represented as a topological feature of the undirected network.
Through function composition, there exists an injective function that
maps a semantic network to an undirected network. Thus, aside from
space constraints, the semantic network construct does not have any
modeling functionality that is not possible with either a directed
or undirected network representation. Two proofs of this idea will
be presented. The first is a proof of the aforementioned function
composition concept. The second is a simpler proof involving an
undirected binary encoding of a semantic network.
Abstract: Bandung city center can be deemed as economic, social and cultural center. However the city center suffers from deterioration. The retail activities tend to shift outward the city center. Numerous idyllic residences changed into business premises in two villages situated in the north part of the city during 1990s, especially after a new highway and flyover opened. According to space syntax theory, the pattern of spatial integration in the urban grid is a prime determinant of movement patterns in the system. The syntactic analysis results show the flyover has insignificant influence on street network in the city center. However the flyover has been generating a major difference in the new commercial area since it has become relatively as strategic as the city center. Besides street network, local government policy, rapid private motorization and particular condition of each site also played important roles in encouraging the current commercial areas to flourish.
Abstract: The experiments were performed in a batch set up
under different concentrations of Cu (II) (0.2 g.l-1 to 0.9 g.l-1), pH (4-
6), temperatures (20oC – 40oC) with varying teak leaves powder (as
biosorbent) dosage of 0.3 g.l-1 to 0.5 g.l-1. The kinetics of interactions
were tested with pseudo first order Lagergran equation and the value
for k1 was found to be 6.909 x 10-3 min-1. The biosorption data gave
a good fit with Langmuir and Fruendlich isotherms and the Langmuir
monolayer capacity (qm) was found to be 166.78 mg. g-1. Similarly
the Freundlich adsorption capacity (Kf) was estimated as 2.49 l g-1.
The mean values of the thermodynamic parameters ΔH, ΔS, and ΔG
were -62.42 KJ. mol-1, -0.219 KJ.mol-1 K-1 and -1.747 KJ.mol-1 at
293 K from a solution containing 0.4 g l-1 of Cu(II) showing the
biosorption to be thermodynamically favourable. These results show
good potentiality of using teak leaves as a biosorbent for the removal
of Cu(II) from aqueous solutions.
Abstract: Background Contact lens (CL) wear can cause
changes in blinking and corneal staining. Aims and Objectives To
determine the effects of CL materials (HEMA and SiHy) on
spontaneous blink rate, blinking patterns and corneal staining after 2
months of wear. Methods Ninety subjects in 3 groups (control,
HEMA and SiHy) were assessed at baseline and 2-months. Blink rate
was recorded using a video camera. Blinking patterns were assessed
with digital camera and slit lamp biomicroscope. Corneal staining
was graded using IER grading scale Results There were no significant
differences in all parameters at baseline. At 2 months, CL wearers
showed significant increment in average blink rate (F1.626, 47.141 =
7.250, p = 0.003; F2,58 = 6.240, p = 0.004) and corneal staining (χ2
2,
n=30 = 31.921, p < 0.001; χ2
2, n=30 = 26.909, p < 0.001). Conclusion
Blinking characteristics and corneal staining were not influence by
soft CL materials.
Abstract: Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system.
Abstract: Regarding to the fast growth of computer, internet, and virtual learning in our country (Iran) and need computer-based learning systems and multimedia tools as an essential part of such education, designing and implementing such systems would help teach different field such as science. This paper describes the basic principle of multimedia. At the end, with a description of learning science to the infant students, the method of this system will be explained.
Abstract: We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the Noisy Microarray, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systholic Boolean Orthonormalizer Network (SBON) to the input bit-plane set and we obtain two orthonormal otput bit-plane sets (in a Boolean sense), we project a set on the other one, by means of an AND operation, and then, 5) we apply re-assembling, and, 6) rescaling. Finally, 7) we apply Inverse DWT-2D and reconstruct a microarray from the modified wavelet coefficients. Denoising results compare favorably to the most of methods in use at the moment.
Abstract: Ambient hydrolysis products in moist air and
hydrolysis kinetics in argon with humidity of RH1.5% for
polycrystalline LiH powders and sintered bulks were investigated by
X-ray diffraction, Raman spectroscopy and gravimetry. The results
showed that the hydrolysis products made up a layered structure of
LiOH•H2O/LiOH/Li2O from surface of the sample to inside. In low
humid argon atmosphere, the primary hydrolysis product was Li2O
rather than LiOH. The hydrolysis kinetic curves of LiH bulks present a
paralinear shape, which could be explained by the “Layer Diffusion
Control" model. While a three-stage hydrolysis kinetic profile was
observed for LiH powders under the same experimental conditions.
The first two sections were similar to that of the bulk samples, and the
third section also presents a linear reaction kinetics but with a smaller
reaction rate compared to the second section because of a larger
exothermic effect for the hydrolysis reaction of LiH powder.
Abstract: Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.
Abstract: The wireless mesh networks (WMNs) are emerging technology in wireless networking as they can serve large scale high speed internet access. Due to its wireless multi-hop feature, wireless mesh network is prone to suffer from many attacks, such as denial of service attack (DoS). We consider a special case of DoS attack which is selective forwarding attack (a.k.a. gray hole attack). In such attack, a misbehaving mesh router selectively drops the packets it receives rom its predecessor mesh router. It is very hard to detect that packet loss is due to medium access collision, bad channel quality or because of selective forwarding attack. In this paper, we present a review of detection algorithms of selective forwarding attack and discuss their advantage & disadvantage. Finally we conclude this paper with open research issues and challenges.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.
Abstract: As the Social network game(SNG) is rising
dramatically worldwide, an interesting aspect has appeared in the
demographic analysis. That is the ratio of the game users by gender.
Although the ratio of male and female users in online game was
60:40% previously, the ratio of male and female users in SNG stood at
47:53% which shows that the ratio of female users is higher than that
of male users. Here, it should be noted that 35% in those 53% female
users are the first-time users of game. This fact suggests that women
who were not interested in game previously has taken an interest in
SNG. Notwithstanding this issue, there have been little studies on the
female users of SNG although there are many studies that analyzed the
tendency of female users- online game play. This study conducted the
analyzed how the game-playing tendency of SNG gamers was
manifested in the game by gender. For that, this study will identify the
tendency of SNG users by gender based on the preceding studies that
analyzed the online game users by gender. The subject of this study
was confined to the farm and urban construction simulation games
which were offered based on the mobile application platform.
Regarding the methodology of study, the first focus group
interview(FGI) was conducted with the male and female users who
had played games on Social network service(SNS) until recently. Later,
the second one-on-one in-depth interview was conducted to gain an
insight into the psychological state of the subjects.
Abstract: A network of coupled stochastic oscillators is
proposed for modeling of a cluster of entangled qubits that is
exploited as a computation resource in one-way quantum
computation schemes. A qubit model has been designed as a
stochastic oscillator formed by a pair of coupled limit cycle
oscillators with chaotically modulated limit cycle radii and
frequencies. The qubit simulates the behavior of electric field of
polarized light beam and adequately imitates the states of two-level
quantum system. A cluster of entangled qubits can be associated
with a beam of polarized light, light polarization degree being
directly related to cluster entanglement degree. Oscillatory network,
imitating qubit cluster, is designed, and system of equations for
network dynamics has been written. The constructions of one-qubit
gates are suggested. Changing of cluster entanglement degree caused
by measurements can be exactly calculated.
Abstract: This article describes a Web pages automatic filtering system. It is an open and dynamic system based on multi agents architecture. This system is built up by a set of agents having each a quite precise filtering task of to carry out (filtering process broken up into several elementary treatments working each one a partial solution). New criteria can be added to the system without stopping its execution or modifying its environment. We want to show applicability and adaptability of the multi-agents approach to the networks information automatic filtering. In practice, most of existing filtering systems are based on modular conception approaches which are limited to centralized applications which role is to resolve static data flow problems. Web pages filtering systems are characterized by a data flow which varies dynamically.
Abstract: Gluconic acid is one of interesting chemical products
in industries such as detergents, leather, photographic, textile, and
especially in food and pharmaceutical industries. Fermentation is an
advantageous process to produce gluconic acid. Mathematical
modeling is important in the design and operation of fermentation
process. In fact, kinetic data must be available for modeling. The
kinetic parameters of gluconic acid production by Aspergillus niger
in batch culture was studied in this research at initial substrate
concentration of 150, 200 and 250 g/l. The kinetic models used were
logistic equation for growth, Luedeking-Piret equation for gluconic
acid formation, and Luedeking-Piret-like equation for glucose
consumption. The Kinetic parameters in the model were obtained by
minimizing non linear least squares curve fitting.