Abstract: In this paper, for the first time, a two-dimensional
(2D) analytical drain current model for sub-100 nm multi-layered
gate material engineered trapezoidal recessed channel (MLGMETRC)
MOSFET: a novel design is presented and investigated using
ATLAS and DEVEDIT device simulators, to mitigate the large gate
leakages and increased standby power consumption that arise due to
continued scaling of SiO2-based gate dielectrics. The twodimensional
(2D) analytical model based on solution of Poisson-s
equation in cylindrical coordinates, utilizing the cylindrical
approximation, has been developed which evaluate the surface
potential, electric field, drain current, switching metric: ION/IOFF
ratio and transconductance for the proposed design. A good
agreement between the model predictions and device simulation
results is obtained, verifying the accuracy of the proposed analytical
model.
Abstract: Subcritical water extraction was investigated as a
novel and alternative technology in the food and pharmaceutical
industry for the separation of Mannitol from olive leaves and its
results was compared with those of Soxhlet extraction. The effects of
temperature, pressure, and flow rate of water and also momentum
and mass transfer dimensionless variables such as Reynolds and
Peclet Numbers on extraction yield and equilibrium partition
coefficient were investigated. The 30-110 bars, 60-150°C, and flow
rates of 0.2-2 mL/min were the water operating conditions. The
results revealed that the highest Mannitol yield was obtained at
100°C and 50 bars. However, extraction of Mannitol was not
influenced by the variations of flow rate. The mathematical modeling
of experimental measurements was also investigated and the model is
capable of predicting the experimental measurements very well. In
addition, the results indicated higher extraction yield for the
subcritical water extraction in contrast to Soxhlet method.
Abstract: A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.
Abstract: We summarize information that facilitates choosing an ontology language for knowledge intensive applications. This paper is a short version of the ontology language state-of-the-art and evolution analysis carried out for choosing an ontology language in the IST Esperonto project. At first, we analyze changes and evolution that took place in the filed of Semantic Web languages during the last years, in particular, around the ontology languages of the RDF/S and OWL family. Second, we present current trends in development of Semantic Web languages, in particular, rule support extensions for Semantic Web languages and emerging ontology languages such as WSMO languages.
Abstract: A novel algorithm for construct a seamless video mosaic of the entire panorama continuously by automatically analyzing and managing feature points, including management of quantity and quality, from the sequence is presented. Since a video contains significant redundancy, so that not all consecutive video images are required to create a mosaic. Only some key images need to be selected. Meanwhile, feature-based methods for mosaicing rely on correction of feature points? correspondence deeply, and if the key images have large frame interval, the mosaic will often be interrupted by the scarcity of corresponding feature points. A unique character of the method is its ability to handle all the problems above in video mosaicing. Experiments have been performed under various conditions, the results show that our method could achieve fast and accurate video mosaic construction. Keywords?video mosaic, feature points management, homography estimation.
Abstract: Text Mining is an important step of Knowledge
Discovery process. It is used to extract hidden information from notstructured
o semi-structured data. This aspect is fundamental because
much of the Web information is semi-structured due to the nested
structure of HTML code, much of the Web information is linked,
much of the Web information is redundant. Web Text Mining helps
whole knowledge mining process to mining, extraction and
integration of useful data, information and knowledge from Web
page contents.
In this paper, we present a Web Text Mining process able to
discover knowledge in a distributed and heterogeneous multiorganization
environment. The Web Text Mining process is based on
flexible architecture and is implemented by four steps able to
examine web content and to extract useful hidden information
through mining techniques. Our Web Text Mining prototype starts
from the recovery of Web job offers in which, through a Text Mining
process, useful information for fast classification of the same are
drawn out, these information are, essentially, job offer place and
skills.
Abstract: In this paper, we construct and implement a new
Steganography algorithm based on learning system to hide a large
amount of information into color BMP image. We have used adaptive
image filtering and adaptive non-uniform image segmentation with
bits replacement on the appropriate pixels. These pixels are selected
randomly rather than sequentially by using new concept defined by
main cases with sub cases for each byte in one pixel. According to
the steps of design, we have been concluded 16 main cases with their
sub cases that covere all aspects of the input information into color
bitmap image. High security layers have been proposed through four
layers of security to make it difficult to break the encryption of the
input information and confuse steganalysis too. Learning system has
been introduces at the fourth layer of security through neural
network. This layer is used to increase the difficulties of the statistical
attacks. Our results against statistical and visual attacks are discussed
before and after using the learning system and we make comparison
with the previous Steganography algorithm. We show that our
algorithm can embed efficiently a large amount of information that
has been reached to 75% of the image size (replace 18 bits for each
pixel as a maximum) with high quality of the output.
Abstract: In this study, Friction Stir Processing (FSP) a recent grain refinement technique was employed to disperse micron-sized (2 *m) SiCp particles into aluminum alloy AA6063. The feasibility to fabricate bulk composites through FSP was analyzed and experiments were conducted at different traverse speeds and wider volumes of the specimens. Micro structural observation were carried out by employing optical microscopy test of the cross sections in both parallel and perpendicular to the tool traverse direction. Mechanical property including micro hardness was evaluated in detail at various regions on the specimen. The composites had an excellent bonding with aluminum alloy substrate and a significant increase of 30% in the micro hardness value of metal matrix composite (MMC) as to that of the base metal has observed. The observations clearly indicate that SiC particles were uniformly distributed within the aluminum matrix.
Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.
Abstract: BioEnergy is an archetypal appropriate technology
and alternate source of energy in rural areas of China, and can meet
the basic need for cooking fuel in rural areas. The paper introduces
with an alternate mean of research that can accelerate the biogas
energy production. Tithonia diversifolia or the Tree marigold can be
hailed as mesophillic anaerobic digestion to increase the production
of more Bioenergy. Tithonia diversifolia is very native to Mexico and
Central America, which can be served as ornamental plants- green
manure and can prevent soil erosion. Tithonia diversifolia is widely
grown and known to Asia, Africa, America and Australia as well.
Nowadays, Considering China’s geographical condition it is found
that Tithonia diversifolia is widely growing plant in the many tropical
and subtropical regions of southern Yunnan- which can have great
usage in accelerating and increasing the Bioenergy production
technology. The paper discussed aiming at proving possibility that
Tithonia diversifolia can be applied in biogas fermentation and its
biogas production potential, the research carried experiment on
Tithonia diversifolia biogas fermentation under the mesophilic
condition (35 Celsius Degree). The result revealed that Tithonia
diversifolia can be used as biogas fermentative material, and 6%
concentration can get the best biogas production, with the TS biogas
production rate 656mL/g and VS biogas production rate 801mL/g. It
is well addressed that Tithonia diversifolia grows wildly in 53
Counties and 9 cities of Yunnan Province, which mainly grows in
form of the road side plants, the edge of the field, countryside, forest
edge, open space; of which demersum-natures can form dense
monospecific beds -causing serious harm to agricultural production
landforms threatening the ecological system as a potentially harmful
exotic plant. There are also found the three types of invasive daisy
alien plants -Eupatorium adenophorum, Eupatorium Odorata and
Tithonia diversifolia in Yunnan Province of China-among them the
Tithonia diversifolia is responsible for causing serious harm to
agricultural production. In this paper we have designed the
experimental explanation of Biogas energy production that requires
anaerobic environment and some microbes; Tithonia diversifolia
plant has been taken into consideration while carrying experiments
and with successful resulting of generating more BioEnergy
emphasizing on the practical applications of Tithonia diversifolia.
This paper aims at- to find a new mechanism to provide a more
scientific basis for the development of this plant herbicides in Biogas
energy and to improve the utilization throughout the world as well.
Abstract: Double heterogeneity of randomly located pebbles in
the core and Coated Fuel Particles (CFPs) in the pebbles are specific
features in pebble bed reactors and usually, because of difficulty to
model with MCNP code capabilities, are neglected. In this study,
characteristics of HTR-10, Tsinghua University research reactor, are
used and not only double heterogeneous but also truncated CFPs and
Pebbles are considered.Firstly, 8335 CFPs are distributed randomly
in a pebble and then the core of reactor is filled with those pebbles
and graphite pebbles as moderator such that 57:43 ratio of fuel and
moderator pebbles is established.Finally, four different core
configurations are modeled. They are Simple Cubic (SC) structure
with truncated pebbles,SC structure without truncated pebble, and
Simple Hexagonal(SH) structure without truncated pebbles and SH
structure with truncated pebbles. Results like effective multiplication
factor (Keff), critical height,etc. are compared with available data.
Abstract: The paper proposes a new concept in developing
collaborative design system. The concept framework involves
applying simulation of supply chain management to collaborative
design called – 'SCM–Based Design Tool'. The system is developed
particularly to support design activities and to integrate all facilities
together. The system is aimed to increase design productivity and
creativity. Therefore, designers and customers can collaborate by the
system since conceptual design. JAG: Jewelry Art Generator based
on artificial intelligence techniques is integrated into the system.
Moreover, the proposed system can support users as decision tool
and data propagation. The system covers since raw material supply
until product delivery. Data management and sharing information are
visually supported to designers and customers via user interface. The
system is developed on Web–assisted product development
environment. The prototype system is presented for Thai jewelry
industry as a system prototype demonstration, but applicable for
other industry.
Abstract: The White Kwao (Pueraria mirifica), a potent phytoestrogenic medicinal plant, has long been use in Thailand as a traditional folkmedicine. However, no scientific information of the direct effect of White Kwao on the development of mammalian embryo was available. Therefore, the purpose of this study was to investigate the effect of White Kwao extract on the in vitro development and implantation rate of mouse embryos. This study was designed into two experiments. In the first experiment, the two-cell stage mouse embryos were collected from the oviduct of superovulated mature female mice, and randomly cultured in three different media, the M16, M16 supplemented with 0.52μg esthinylestradiol-17β, and M16 supplemented with 10 mg/ml White Kwao extract. The culture was incubated in CO2 incubator at 37 oC . After the embryos were cultivated, the developments of embryos were observed every 24 hours for 5 days. The development rate of embryos from the two-cell stage to blastocyst stage in the media was with White Kwao was significantly higher (p
Abstract: Voltage collapse is instability of heavily loaded electric
power systems that cause to declining voltages and blackout. Power
systems are predicated to become more heavily loaded in the future
decade as the demand for electric power rises while economic and
environmental concerns limit the construction of new transmission
and generation capacity. Heavily loaded power systems are closer to
their stability limits and voltage collapse blackouts will occur if
suitable monitoring and control measures are not taken. To control
transmission lines, it can be used from FACTS devices.
In this paper Harmony search algorithm (HSA) and Genetic
Algorithm (GA) have applied to determine optimal location of
FACTS devices in a power system to improve power system stability.
Three types of FACTS devices (TCPAT, UPFS, and SVC) have been
introduced. Bus under voltage has been solved by controlling reactive
power of shunt compensator. Also a combined series-shunt
compensators has been also used to control transmission power flow
and bus voltage simultaneously.
Different scenarios have been considered. First TCPAT, UPFS, and
SVC are placed solely in transmission lines and indices have been
calculated. Then two types of above controller try to improve
parameters randomly. The last scenario tries to make better voltage
stability index and losses by implementation of three types controller
simultaneously. These scenarios are executed on typical 34-bus test
system and yields efficiency in improvement of voltage profile and
reduction of power losses; it also may permit an increase in power
transfer capacity, maximum loading, and voltage stability margin.
Abstract: New generation mobile communication networks have
the ability of supporting triple play. In order that, Orthogonal
Frequency Division Multiplexing (OFDM) access techniques have
been chosen to enlarge the system ability for high data rates
networks. Many of cross-layer modeling and optimization schemes
for Quality of Service (QoS) and capacity of downlink multiuser
OFDM system were proposed. In this paper, the Maximum Weighted
Capacity (MWC) based resource allocation at the Physical (PHY)
layer is used. This resource allocation scheme provides a much better
QoS than the previous resource allocation schemes, while
maintaining the highest or nearly highest capacity and costing similar
complexity. In addition, the Delay Satisfaction (DS) scheduling at the
Medium Access Control (MAC) layer, which allows more than one
connection to be served in each slot is used. This scheduling
technique is more efficient than conventional scheduling to
investigate both of the number of users as well as the number of
subcarriers against system capacity. The system will be optimized for
different operational environments: the outdoor deployment scenarios
as well as the indoor deployment scenarios are investigated and also
for different channel models. In addition, effective capacity approach
[1] is used not only for providing QoS for different mobile users, but
also to increase the total wireless network's throughput.
Abstract: In this work, a Modified Functional Link Artificial
Neural Network (M-FLANN) is proposed which is simpler than a
Multilayer Perceptron (MLP) and improves upon the universal
approximation capability of Functional Link Artificial Neural
Network (FLANN). MLP and its variants: Direct Linear Feedthrough
Artificial Neural Network (DLFANN), FLANN and
M-FLANN have been implemented to model a simulated Water Bath
System and a Continually Stirred Tank Heater (CSTH). Their
convergence speed and generalization ability have been compared.
The networks have been tested for their interpolation and
extrapolation capability using noise-free and noisy data. The results
show that M-FLANN which is computationally cheap, performs
better and has greater generalization ability than other networks
considered in the work.
Abstract: The finite-difference time-domain (FDTD) method is
one of the most widely used computational methods in
electromagnetic. This paper describes the design of two-dimensional
(2D) FDTD simulation software for transverse magnetic (TM)
polarization using Berenger's split-field perfectly matched layer
(PML) formulation. The software is developed using Matlab
programming language. Numerical examples validate the software.
Abstract: This paper looks into areas not covered by prominent
Agent-Oriented Software Engineering (AOSE) methodologies.
Extensive paper review led to the identification of two issues, first
most of these methodologies almost neglect semantic web and
ontology. Second, as expected, each one has its strength and
weakness and may focus on some phases of the development
lifecycle but not all of the phases. The work presented here builds
extensions to a highly regarded AOSE methodology (MaSE) in order
to cover the areas that this methodology does not concentrate on. The
extensions include introducing an ontology stage for semantic
representation and integrating early requirement specification from a
methodology which mainly focuses on that. The integration involved
developing transformation rules (with the necessary handling of nonmatching
notions) between the two sets of representations and
building the software which automates the transformation. The
application of this integration on a case study is also presented in the
paper. The main flow of MaSE stages was changed to smoothly
accommodate the new additions.
Abstract: The internet has become an attractive avenue for
global e-business, e-learning, knowledge sharing, etc. Due to
continuous increase in the volume of web content, it is not practically
possible for a user to extract information by browsing and integrating
data from a huge amount of web sources retrieved by the existing
search engines. The semantic web technology enables advancement
in information extraction by providing a suite of tools to integrate
data from different sources. To take full advantage of semantic web,
it is necessary to annotate existing web pages into semantic web
pages. This research develops a tool, named OWIE (Ontology-based
Web Information Extraction), for semantic web annotation using
domain specific ontologies. The tool automatically extracts
information from html pages with the help of pre-defined ontologies
and gives them semantic representation. Two case studies have been
conducted to analyze the accuracy of OWIE.