Abstract: Modern managements of water distribution system
(WDS) need water quality models that are able to accurately predict
the dynamics of water quality variations within the distribution system
environment. Before water quality models can be applied to solve
system problems, they should be calibrated. Although former
researchers use GA solver to calibrate relative parameters, it is
difficult to apply on the large-scale or medium-scale real system for
long computational time. In this paper a new method is designed
which combines both macro and detailed model to optimize the water
quality parameters. This new combinational algorithm uses radial
basis function (RBF) metamodeling as a surrogate to be optimized for
the purpose of decreasing the times of time-consuming water quality
simulation and can realize rapidly the calibration of pipe wall reaction
coefficients of chlorine model of large-scaled WDS. After two cases
study this method is testified to be more efficient and promising, and
deserve to generalize in the future.
Abstract: Adhesion to the human intestinal cell is considered
as one of the main selection criteria of lactic acid bacteria for
probiotic use. The adhesion ability of two Bifidobacteriums strains
Bifidobacterium longum BB536 and Bifidobacterium
psudocatenulatum G4 was done using HT-29 human epithelium
cell line as in vitro study. Four different level of pH were used 5.6,
5.7, 6.6, and 6.8 with four different times 15, 30, 60, and 120 min.
Adhesion was quantified by counting the adhering bacteria after
Gram staining. The adhesion of B. longum BB536 was higher than
B. psudocatenulatum G4. Both species showed significant
different in the adhesion properties at the factors tested. The
highest adhesion for both Bifidobacterium was observed at 120
min and the low adhesion was in 15 min. The findings of this
study will contribute to the introduction of new effective probiotic
strain for future utilization.
Abstract: This paper includes a review of three physics simulation packages that can be used to provide researchers with a virtual ground for modeling, implementing and simulating complex models, as well as testing their control methods with less cost and time of development. The inverted pendulum model was used as a test bed for comparing ODE, DANCE and Webots, while Linear State Feedback was used to control its behavior. The packages were compared with respect to model creation, solving systems of differential equation, data storage, setting system variables, control the experiment and ease of use. The purpose of this paper is to give an overview about our experience with these environments and to demonstrate some of the benefits and drawbacks involved in practice for each package.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.
Abstract: Information hiding, especially watermarking is a
promising technique for the protection of intellectual property rights.
This technology is mainly advanced for multimedia but the same has
not been done for text. Web pages, like other documents, need a
protection against piracy. In this paper, some techniques are
proposed to show how to hide information in web pages using some
features of the markup language used to describe these pages. Most
of the techniques proposed here use the white space to hide
information or some varieties of the language in representing
elements. Experiments on a very small page and analysis of five
thousands web pages show that these techniques have a wide
bandwidth available for information hiding, and they might form a
solid base to develop a robust algorithm for web page watermarking.
Abstract: The main purpose of this study is to provide a detailed
statistical overview of the time and regional distribution, relative
timing occurrence of economic crises and government changes in 51
economies over the 1990–2007 periods. At the same time, the
predictive power of the economic crises on set government changes
will be examined using “signal approach".
The result showed that the percentage of government changes is
highest in transition economies (86 percent of observations) and
lowest in Latin American economies (39 percent of observations).
The percentages of government changes are same in both developed
and developing countries (43 percent of observations). However,
average crises per year (frequency of crises) are higher (lower) in
developing (developed) countries than developed (developing)
countries. Also, the predictive power of economic crises about the
onset of a government change is highest in Transition economies (81
percent) and lowest in Latin American countries (30 percent). The
predictive power of economic crises in developing countries (43
percent) is lower than developed countries (55 percent).
Abstract: Single-pole switching scheme is widely used in the
Extra High Voltage system. However, the substantial negativesequence
current injected to the turbine-generators imposes the
electromagnetic (E/M) torque of double system- frequency
components during the dead time (between single-pole clearing and
line reclosing). This would induce supersynchronous resonance
(SPSR) torque amplifications on low pressure turbine generator
blades and even lead to fatigue damage. This paper proposes the
design of a mechanical filter (MF) with natural frequency close to
double-system frequency. From the simulation results, it is found that
such a filter not only successfully damps the resonant effect, but also
has the characteristics of feasibility and compact.
Abstract: Small tanks, the ancient man-made rain water storage
systems, support the pheasant life and agriculture of the dry zone of
Sri Lanka. Many small tanks were abandoned with time due to
various reasons. Such tanks, rehabilitated in the recent past, were
found to be less sustainable and most of these rehabilitation
approaches have failed. The objective of this research is to assess the
impact of the rehabilitation approaches in the management of small
tanks in the Kurunegala District of Sri Lanka with respect to eight
small tanks. A Sustainability index was developed using seven
indicators representing the ability and commitment of the villagers to
maintain these tanks. The sustainability index of the eight tanks
varied between 79.2 and 47.2 out of a total score of 100. The
conclusion is that, the approaches used for tank rehabilitation have a
significant effect on the sustainability of the management of these
small tanks.
Abstract: Crucial information barely visible to the human eye is
often embedded in a series of low resolution images taken of the
same scene. Super resolution reconstruction is the process of
combining several low resolution images into a single higher
resolution image. The ideal algorithm should be fast, and should add
sharpness and details, both at edges and in regions without adding
artifacts. In this paper we propose a super resolution blind
reconstruction technique for linearly degraded images. In our
proposed technique the algorithm is divided into three parts an image
registration, wavelets based fusion and an image restoration. In this
paper three low resolution images are considered which may sub
pixels shifted, rotated, blurred or noisy, the sub pixel shifted images
are registered using affine transformation model; A wavelet based
fusion is performed and the noise is removed using soft thresolding.
Our proposed technique reduces blocking artifacts and also
smoothens the edges and it is also able to restore high frequency
details in an image. Our technique is efficient and computationally
fast having clear perspective of real time implementation.
Abstract: In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.
Abstract: This study was investigated on sampling and
analyzing water quality in water reservoir & water tower installed in
two kind of residential buildings and school facilities. Data of water
quality was collected for correlation analysis with frequency of
sanitization of water reservoir through questioning managers of
building about the inspection charts recorded on equipment for water
reservoir. Statistical software packages (SPSS) were applied to the
data of two groups (cleaning frequency and water quality) for
regression analysis to determine the optimal cleaning frequency of
sanitization. The correlation coefficient (R) in this paper represented
the degree of correlation, with values of R ranging from +1 to -1.After
investigating three categories of drinking water users; this study found
that the frequency of sanitization of water reservoir significantly
influenced the water quality of drinking water. A higher frequency of
sanitization (more than four times per 1 year) implied a higher quality
of drinking water. Results indicated that sanitizing water reservoir &
water tower should at least twice annually for achieving the aim of
safety of drinking water.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: In any distributed systems, process scheduling plays a
vital role in determining the efficiency of the system. Process scheduling algorithms are used to ensure that the components of the
system would be able to maximize its utilization and able to complete all the processes assigned in a specified period of time.
This paper focuses on the development of comparative simulator for distributed process scheduling algorithms. The objectives of the works that have been carried out include the development of the
comparative simulator, as well as to implement a comparative study
between three distributed process scheduling algorithms; senderinitiated,
receiver-initiated and hybrid sender-receiver-initiated
algorithms. The comparative study was done based on the Average Waiting Time (AWT) and Average Turnaround Time (ATT) of the
processes involved. The simulation results show that the performance of the algorithms depends on the number of nodes in the system.
Abstract: The flash memory has many advantages such as low power consumption, strong shock resistance, fast I/O and non-volatility. And it is increasingly used in the mobile storage device. The YAFFS, one of the NAND flash file system, is widely used in the embedded device. However, the existing YAFFS takes long time to mount the file system because it scans whole spare areas in all pages of NAND flash memory. In order to solve this problem, we propose a new content-based flash file system using a mounting time reduction technique. The proposed method only scans partial spare areas of some special pages by using content-based block management. The experimental results show that the proposed method reduces the average mounting time by 87.2% comparing with JFFS2 and 69.9% comparing with YAFFS.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: The multidelays linear control systems described by
difference differential equations are often studied in modern control
theory. In this paper, the delay-independent stabilization algebraic
criteria and the theorem of delay-independent stabilization for linear
systems with multiple time-delays are established by using the
Lyapunov functional and the Riccati algebra matrix equation in the
matrix theory. An illustrative example and the simulation result, show
that the approach to linear systems with multiple time-delays is
effective.
Abstract: Using vision based solution in intelligent vehicle application often needs large memory to handle video stream and image process which increase complexity of hardware and software. In this paper, we present a FPGA implement of a vision based lane departure warning system. By taking frame of videos, the line gradient of line is estimated and the lane marks are found. By analysis the position of lane mark, departure of vehicle will be detected in time. This idea has been implemented in Xilinx Spartan6 FPGA. The lane departure warning system used 39% logic resources and no memory of the device. The average availability is 92.5%. The frame rate is more than 30 frames per second (fps).
Abstract: Many exist studies always use Markov decision
processes (MDPs) in modeling optimal route choice in
stochastic, time-varying networks. However, taking many
variable traffic data and transforming them into optimal route
decision is a computational challenge by employing MDPs in
real transportation networks. In this paper we model finite
horizon MDPs using directed hypergraphs. It is shown that the
problem of route choice in stochastic, time-varying networks
can be formulated as a minimum cost hyperpath problem, and
it also can be solved in linear time. We finally demonstrate the
significant computational advantages of the introduced
methods.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: This paper focuses upon three such painters working in
France from this time and their representations both of their host
country in which they found themselves displaced, and of their
homeland which they represent through refracted memories from their
new perspective in Europe. What is their representation of France and
China´╝ÅTaiwan? Is it Otherness or an origin?
This paper also attempts to explore the three artists- diasporic lives
and to redefine their transnational identities. Hou Chin-lang, the
significance of his multiple-split images serve to highlight the intricate
relationships between his work and the surrounding family, and to
reveal his identity of his Taiwan “homeland". Yin Xin takes paintings
from the Western canon and subjects them to a process of
transformation through Chinese imagery. In the same period, Lin
Li-ling, transforms the transnational spirit of Yin Xin to symbolic
codes with neutered female bodies and tatoos, thus creates images that
challenge the boundaries of both gender and nationality.