Abstract: Among the various cooling processes in industrial
applications such as: electronic devices, heat exchangers, gas
turbines, etc. Gas turbine blades cooling is the most challenging one.
One of the most common practices is using ribbed wall because of
the boundary layer excitation and therefore making the ultimate
cooling. Vortex formation between rib and channel wall will result in
a complicated behavior of flow regime. At the other hand, selecting
the most efficient method for capturing the best results comparing to
experimental works would be a fascinating issue. In this paper 4
common methods in turbulence modeling: standard k-e, rationalized
k-e with enhanced wall boundary layer treatment, k-w and RSM
(Reynolds stress model) are employed to a square ribbed channel to
investigate the separation and thermal behavior of the flow in the
channel. Finally all results from different methods which are used in
this paper will be compared with experimental data available in
literature to ensure the numerical method accuracy.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: In this article we propose to model Net-banking
system by game theory. We adopt extensive game to model our web
application. We present the model in term of players and strategy.
We present UML diagram related the protocol game.
Abstract: One of the most challenges for hard surface cleaning product is to get rid of soap scum, a filmy sticky layer in the bathroom. The deposits of soap scum can be removed by using a proper surfactant solution with chelating agent. Unfortunately, the conventional chelating agent, ethylenediamine tetraacetic acid (EDTA), has low biodegradability, which can be tolerance in water resources and harmful to aquatic animal and microorganism. In this study, two biodegradable chelating agents, ethylenediamine disuccinic acid (EDDS) and glutamic acid diacetic acid (GLDA) were introduced as a replacement of EDTA. The result shows that using GLDA with amphoteric surfactant gave the highest equilibrium solubility of soap scum.
Abstract: In the oil and gas industry, energy prediction can help
the distributor and customer to forecast the outgoing and incoming
gas through the pipeline. It will also help to eliminate any
uncertainties in gas metering for billing purposes. The objective of
this paper is to develop Neural Network Model for energy
consumption and analyze the performance model. This paper
provides a comprehensive review on published research on the
energy consumption prediction which focuses on structures and the
parameters used in developing Neural Network models. This paper is
then focused on the parameter selection of the neural network
prediction model development for energy consumption and analysis
on the result. The most reliable model that gives the most accurate
result is proposed for the prediction. The result shows that the
proposed neural network energy prediction model is able to
demonstrate an adequate performance with least Root Mean Square
Error.
Abstract: The article deals with experimental and numerical
investigation of axi-symmetric subsonic air to air ejector with
diffuser adapted for boundary layer suction. The diffuser, which is
placed behind the mixing chamber of the ejector, has high divergence
angle and therefore low efficiency. To increase the efficiency, the
diffuser is equipped with slot enabling boundary layer suction. The
effect of boundary layer suction on flow in ejector, static pressure
distribution on the mixing chamber wall and characteristic were
measured and studied numerically. Both diffuser and ejector
efficiency were evaluated. The diffuser efficiency was increased,
however, the efficiency of ejector itself remained low.
Abstract: Investigation of soil properties like Cation Exchange
Capacity (CEC) plays important roles in study of environmental
reaserches as the spatial and temporal variability of this property
have been led to development of indirect methods in estimation of
this soil characteristic. Pedotransfer functions (PTFs) provide an
alternative by estimating soil parameters from more readily available
soil data. 70 soil samples were collected from different horizons of
15 soil profiles located in the Ziaran region, Qazvin province, Iran.
Then, multivariate regression and neural network model (feedforward
back propagation network) were employed to develop a
pedotransfer function for predicting soil parameter using easily
measurable characteristics of clay and organic carbon. The
performance of the multivariate regression and neural network model
was evaluated using a test data set. In order to evaluate the models,
root mean square error (RMSE) was used. The value of RMSE and
R2 derived by ANN model for CEC were 0.47 and 0.94 respectively,
while these parameters for multivariate regression model were 0.65
and 0.88 respectively. Results showed that artificial neural network
with seven neurons in hidden layer had better performance in
predicting soil cation exchange capacity than multivariate regression.
Abstract: This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].
Abstract: In order to answer the general question: “What does a simple agent with a limited life-time require for constructing a useful representation of the environment?" we propose a robot platform including the simplest probabilistic sensory and motor layers. Then we use the platform as a test-bed for evaluation of the navigational capabilities of the robot with different “brains". We claim that a protocognitive behavior is not a consequence of highly sophisticated sensory–motor organs but instead emerges through an increment of the internal complexity and reutilization of the minimal sensory information. We show that the most fundamental robot element, the short-time memory, is essential in obstacle avoidance. However, in the simplest conditions of no obstacles the straightforward memoryless robot is usually superior. We also demonstrate how a low level action planning, involving essentially nonlinear dynamics, provides a considerable gain to the robot performance dynamically changing the robot strategy. Still, however, for very short life time the brainless robot is superior. Accordingly we suggest that small organisms (or agents) with short life-time does not require complex brains and even can benefit from simple brain-like (reflex) structures. To some extend this may mean that controlling blocks of modern robots are too complicated comparative to their life-time and mechanical abilities.
Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
Abstract: In situ observation of absorption spectral change of
heptil viologen cation radical (HV+.) was performed by slab optical
waveguide (SOWG) spectroscopy utilizing indium-tin-oxide (ITO)
electrodes. Synchronizing with electrochemical techniques, we
observed the adsorption process of HV+.on the ITO electrode. In this
study, we carried out the ITO-SOWG observations using KBr aqueous
solution containing different concentration of HV to investigate the
concentration dependent spectral change. A few specific absorption
bands, which indicated HV+.existed as both monomer and dimer on
ITO electrode surface with a monolayer or a few layers deposition,
were observed in UV-visible region. The change in the peak position
of the absorption spectra from adsorption species of HV+. were
correlated with the concentration of HV as well as the electrode
potential.
Abstract: Traditional optical networks are gradually evolving towards intelligent optical networks due to the need for faster bandwidth provisioning, protection and restoration of the network that can be accomplished with devices like optical switch, add drop multiplexer and cross connects. Since dense wavelength multiplexing forms the physical layer for intelligent optical networking, the roll of high speed all optical switch is important. This paper analyzes such an ultra-high speed polymer electro-optic switch. The performances of the 2x2 optical waveguide switch with rectangular, triangular and trapezoidal grating profiles on various device parameters are analyzed. The simulation result shows that trapezoidal grating is the optimized structure which has the coupling length of 81μm and switching voltage of 11V for the operating wavelength of 1550nm. The switching time for this proposed switch is 0.47 picosecond. This makes the proposed switch to be an important element in the intelligent optical network.
Abstract: Linear stability analysis of wake-shear layers in twophase
shallow flows is performed in the present paper. Twodimensional
shallow water equations are used in the analysis. It is
assumed that the fluid contains uniformly distributed solid particles.
No dynamic interaction between the carrier fluid and particles is
expected in the initial moment. The stability calculations are
performed for different values of the particle loading parameter and
two other parameters which characterize the velocity ratio and the
velocity deficit. The results show that the particle loading parameter
has a stabilizing effect on the flow while the increase in the velocity
ratio or in the velocity deficit destabilizes the flow.
Abstract: In this paper, we propose an effective relay
communication for layered video transmission as an alternative to
make the most of limited resources in a wireless communication
network where loss often occurs. Relaying brings stable multimedia
services to end clients, compared to multiple description coding
(MDC). Also, retransmission of only parity data about one or more
video layer using channel coder to the end client of the relay device is
paramount to the robustness of the loss situation. Using these
methods in resource-constrained environments, such as real-time user
created content (UCC) with layered video transmission, can provide
high-quality services even in a poor communication environment.
Minimal services are also possible. The mathematical analysis shows
that the proposed method reduced the probability of GOP loss rate
compared to MDC and raptor code without relay. The GOP loss rate
is about zero, while MDC and raptor code without relay have a GOP
loss rate of 36% and 70% in case of 10% frame loss rate.
Abstract: Medical applications are among the most impactful
areas of microrobotics. The ultimate goal of medical microrobots is
to reach currently inaccessible areas of the human body and carry out
a host of complex operations such as minimally invasive surgery
(MIS), highly localized drug delivery, and screening for diseases at
their very early stages. Miniature, safe and efficient propulsion
systems hold the key to maturing this technology but they pose
significant challenges. A new type of propulsion developed recently,
uses multi-flagella architecture inspired by the motility mechanism of
prokaryotic microorganisms. There is a lack of efficient methods for
designing this type of propulsion system. The goal of this paper is to
overcome the lack and this way, a numerical strategy is proposed to
design multi-flagella propulsion systems. The strategy is based on the
implementation of the regularized stokeslet and rotlet theory, RFT
theory and new approach of “local corrected velocity". The effects of
shape parameters and angular velocities of each flagellum on overall
flow field and on the robot net forces and moments are considered.
Then a multi-layer perceptron artificial neural network is designed
and employed to adjust the angular velocities of the motors for
propulsion control. The proposed method applied successfully on a
sample configuration and useful demonstrative results is obtained.
Abstract: Axisymmetric vibration of an infinite Pyrocomposite
circular hollow cylinder made of inner and outer pyroelectric layer of
6mm-class bonded together by a Linear Elastic Material with Voids
(LEMV) layer is studied. The exact frequency equation is obtained
for the traction free surfaces with continuity condition at the
interfaces. Numerical results in the form of data and dispersion
curves for the first and second mode of the axisymmetric vibration of
the cylinder BaTio3 / Adhesive / BaTio3 by taking the Adhesive layer
as an existing Carbon Fibre Reinforced Polymer (CFRP) are
compared with a hypothetical LEMV layer with and without voids
and as well with a pyroelectric hollow cylinder. The damping is
analyzed through the imaginary parts of the complex frequencies.
Abstract: In synchronized games players make their moves simultaneously
rather than alternately. Synchronized Quadromineering is
the synchronized version of Quadromineering, a variants of a classical
two-player combinatorial game called Domineering. Experimental
results for small m × n boards (with m + n < 15) and some
theoretical results for general k × n boards (with k = 4, 5, 6) are
presented. Moreover, some Synchronized Quadromineering variants
are also investigated.
Abstract: We report here, the results of molecular dynamics
simulation of p-doped (Ga-face)GaN over n-doped (Siface)(
0001)4H-SiC hetero-epitaxial material system with one-layer
each of Ga-flux and (Al-face)AlN, as the interface materials, in the
form of, the total Density of States (DOS). It is found that the total
DOS at the Fermi-level for the heavily p-doped (Ga-face)GaN and ndoped
(Si-face)4H-SiC hetero-epitaxial system, with one layer of
(Al-face)AlN as the interface material, is comparatively higher than
that of the various cases studied, indicating that there could be good
vertical conduction across the (Ga-face)GaN over (Si-face)(0001)4HSiC
hetero-epitaxial material system.
Abstract: In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.