Abstract: In this study, the kinetics of osmotic dehydration of melons (Tille variety) in a ternary system followed by air-drying for preserving melons in the summer to be used in the winter were investigated. The effect of different osmotic solution concentrations 30, 40 and 50% (w/w) of sucrose with 10% NaCl salt and fruit to solution ratios 1:4, 1:5 and 1:6 on the mass transfer kinetics during osmotic dehydration of melon in ternary solution namely sucrosesalt- water followed by air-drying were studied. The diffusivity of water during air-drying was enhanced after the fruit samples were immersed in the osmotic solution after 60 min. Samples non-treated and pre-treated during one hour in osmotic solutions with 60% (w/w) of sucrose with 10% NaCl salt and fruit to solution ratio of 1:4 were dried in a hot air-dryer at 60oC (2 m/s) until equilibrium was achieved.
Abstract: In this paper we present an autoregressive model with
neural networks modeling and standard error backpropagation
algorithm training optimization in order to predict the gross domestic
product (GDP) growth rate of four countries. Specifically we propose
a kind of weighted regression, which can be used for econometric
purposes, where the initial inputs are multiplied by the neural
networks final optimum weights from input-hidden layer after the
training process. The forecasts are compared with those of the
ordinary autoregressive model and we conclude that the proposed
regression-s forecasting results outperform significant those of
autoregressive model in the out-of-sample period. The idea behind
this approach is to propose a parametric regression with weighted
variables in order to test for the statistical significance and the
magnitude of the estimated autoregressive coefficients and
simultaneously to estimate the forecasts.
Abstract: Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. Fueling the attention have been the possibilities of international agreements to reduce greenhouse gas emissions, electricity sector restructuring, high power reliability requirements for certain activities, and concern about easing transmission and distribution capacity bottlenecks and congestion. So it is necessary that impact of these kinds of generators on distribution feeder reconfiguration would be investigated. This paper presents an approach for distribution reconfiguration considering Distributed Generators (DGs). The objective function is summation of electrical power losses A Tabu search optimization is used to solve the optimal operation problem. The approach is tested on a real distribution feeder.
Abstract: Partitions can play a significant role in minimising cochannel
interference of Wireless LANs by attenuating signals across
room boundaries. This could pave the way towards higher density
deployments in home and office environments through spatial
channel reuse. Yet, due to protocol limitations, the latest incantation
of IEEE 802.11 standard is still unable to take advantage of this fact:
Despite having clearly adequate Signal to Interference Ratio (SIR)
over co-channel neighbouring networks in other rooms, its goodput
falls significantly lower than its maximum in the absence of cochannel
interferers. In this paper, we describe how this situation can
be remedied via modest modifications to the standard.
Abstract: In this paper we consider a nonlinear feedback control
called augmented automatic choosing control (AACC) using the
gradient optimization automatic choosing functions for nonlinear
systems. Constant terms which arise from sectionwise linearization
of a given nonlinear system are treated as coefficients of a stable
zero dynamics. Parameters included in the control are suboptimally
selected by expanding a stable region in the sense of Lyapunov
with the aid of the genetic algorithm. This approach is applied to
a field excitation control problem of power system to demonstrate
the splendidness of the AACC. Simulation results show that the new
controller can improve performance remarkably well.
Abstract: In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.
Abstract: Cellular communication is being widely used by all
over the world. The users of handsets are increasing due to the
request from marketing sector. The important aspect that has to be
touch in this paper is about the security system of cellular
communication. It is important to provide users with a secure channel
for communication. A brief description of the new GSM cellular
network architecture will be provided. Limitations of cellular
networks, their security issues and the different types of attacks will
be discussed. The paper will go over some new security mechanisms
that have been proposed by researchers. Overall, this paper clarifies
the security system or services of cellular communication using
GSM. Three Malaysian Communication Companies were taken as
Case study in this paper.
Abstract: In this paper the performance of unified power flow
controller is investigated in controlling the flow of po wer over the
transmission line. Voltage sources model is utilized to study the
behaviour of the UPFC in regulating the active, reactive power and
voltage profile. This model is incorporated in Newton Raphson
algorithm for load flow studies. Simultaneous method is employed
in which equations of UPFC and the power balance equations of
network are combined in to one set of non-linear algebraic equations.
It is solved according to the Newton raphson algorithm. Case studies
are carried on standard 5 bus network. Simulation is done in Matlab.
The result of network with and without using UPFC are compared in
terms of active and reactive power flows in the line and active and
reactive power flows at the bus to analyze the performance of UPFC.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: The objective of the present investigation was to
evaluate the morphology of Escherchia coli bacteria in interaction
with SiO2 nanoparticles.
This study was made by atomic force microscopy and quartz
crystal microbalance using SiO2 nanoparticles with 10nm, 50nm and
100nm diameter and bacteria immobilized on polyelectrolyte
multilayer films obtained by spin coating or by “layer by layer”
(LbL) method.
Abstract: A person-to-person information sharing is easily realized
by P2P networks in which servers are not essential. Leakage
of information, which are caused by malicious accesses for P2P
networks, has become a new social issues. To prevent information
leakage, it is necessary to detect and block traffics of P2P software.
Since some P2P softwares can spoof port numbers, it is difficult to
detect the traffics sent from P2P softwares by using port numbers.
It is more difficult to devise effective countermeasures for detecting
the software because their protocol are not public.
In this paper, a discriminating method of network applications
based on communication characteristics of application messages
without port numbers is proposed. The proposed method is based
on an assumption that there can be some rules about time intervals
to transmit messages in application layer and the number of necessary
packets to send one message. By extracting the rule from network
traffic, the proposed method can discriminate applications without
port numbers.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: Transcription factor p53 has a powerful tumor
suppressing function that is associated with many cancers. However,
p53 of the molecular weight was higher make the limitation across to
skin or cell membrane. Thymidine dinucleotide (pTT), an
oligonucleotide, can activate the p53 transcription factor. pTT is a
hydrophilic and negative charge oligonucleotide, which delivery in to
cell membrane need an appropriate carrier. The aim of this study was
to improve the bioavailability of the nucleotide fragment, thymidine
dinucleotide (pTT), using elasic liposome carriers to deliver the drug
into the skin. The study demonstrate that dioleoylphosphocholine
(DOPC) incorporated with sodium cholate at molar ratio 1:1 can
archived the particle size about 220 nm. This elastic liposome could
penetration through skin from stratum corneum to whole epidermis by
confocal laser scanning microscopy (CLSM). Moreover, we observed
the the slight increase in generation of p53 by western blot.
Abstract: The steady-state operation of maintaining voltage
stability is done by switching various controllers scattered all over
the power network. When a contingency occurs, whether forced or
unforced, the dispatcher is to alleviate the problem in a minimum
time, cost, and effort. Persistent problem may lead to blackout. The
dispatcher is to have the appropriate switching of controllers in terms
of type, location, and size to remove the contingency and maintain
voltage stability. Wrong switching may worsen the problem and that
may lead to blackout. This work proposed and used a Fuzzy CMeans
Clustering (FCMC) to assist the dispatcher in the decision
making. The FCMC is used in the static voltage stability to map
instantaneously a contingency to a set of controllers where the types,
locations, and amount of switching are induced.
Abstract: In this paper, we have focused on study of swelling kinetics and salt-sensitivity behavior of a superabsorbing hydrogel based on carboxymethylcellulose (CMC) and acrylic acid and 2- Buthyl methacrylate. The swelling kinetics of the hydrogels with various particle sizes was preliminary investigated as well. The swelling of the hydrogel showed a second order kinetics of swelling in water. In addition, swelling measurements of the synthesized hydrogels in various chloride salt solutions was measured. Results indicated that a swelling-loss with an increase in the ionic strength of the salt solutions.
Abstract: This paper presents the design and implementation of
the WebGD, a CORBA-based document classification and retrieval
system on Internet. The WebGD makes use of such techniques as Web,
CORBA, Java, NLP, fuzzy technique, knowledge-based processing
and database technology. Unified classification and retrieval model,
classifying and retrieving with one reasoning engine and flexible
working mode configuration are some of its main features. The
architecture of WebGD, the unified classification and retrieval model,
the components of the WebGD server and the fuzzy inference engine
are discussed in this paper in detail.
Abstract: This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.
Abstract: This paper proposes the use of Bayesian belief
networks (BBN) as a higher level of health risk assessment for a
dumping site of lead battery smelter factory. On the basis of the
epidemiological studies, the actual hospital attendance records and
expert experiences, the BBN is capable of capturing the probabilistic
relationships between the hazardous substances and their adverse
health effects, and accordingly inferring the morbidity of the adverse
health effects. The provision of the morbidity rates of the related
diseases is more informative and can alleviate the drawbacks of
conventional methods.
Abstract: Recently, various services such as television and the
Internet have come to be received through various terminals.
However, we could gain greater convenience by receiving these
services through cellular phone terminals when we go out and then
continuing to receive the same services through a large screen digital
television after we have come home. However, it is necessary to go
through the same authentication processing again when using TVs
after we have come home. In this study, we have developed an
authentication method that enables users to switch terminals in
environments in which the user receives service from a server through
a terminal. Specifically, the method simplifies the authentication of
the server side when switching from one terminal to another terminal
by using previously authenticated information.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.