Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: In this paper, we study the instability of the zero solution to a nonlinear differential equation with variable delay. By using the Lyapunov functional approach, some sufficient conditions for instability of the zero solution are obtained.
Abstract: The computer has become an essential tool in modern
life, and the combined use of a computer with a projector is very
common in teaching and presentations. However, as typical computer
operating devices involve a mouse or keyboard, when making
presentations, users often need to stay near the computer to execute
functions such as changing pages, writing, and drawing, thus, making
the operation time-consuming, and reducing interactions with the
audience. This paper proposes a laser pointer interaction system able
to simulate mouse functions in order that users need not remain near
the computer, but can directly use laser pointer operations from at a
distance. It can effectively reduce the users- time spent by the
computer, allowing for greater interactions with the audience.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: The lifelong learning is a crucial element in the
modernization of European education and training systems. The most
important actors in the development process of the lifelong learning
are the trainers, whose professional characteristics need new
competences and skills in the current labour market. The main
objective of this paper is to establish an importance ranking of the
new competences, capabilities and skills that the lifelong learning
Spanish trainers must possess nowadays. A wide study of secondary
sources has allowed the design of a questionnaire that organizes the
trainer-s skills and competences. The e-Delphi method is used for
realizing a creative, individual and anonymous evaluation by experts
on the importance ranking that presents the criteria, sub-criteria and
indicators of the e-Delphi questionnaire. Twenty Spanish experts in
the lifelong learning have participated in two rounds of the e-
DELPHI method. In the first round, the analysis of the experts-
evaluation has allowed to establish the ranking of the most
importance criteria, sub-criteria and indicators and to eliminate the
least valued. The minimum level necessary to reach the consensus
among experts has been achieved in the second round.
Abstract: Recently electric vehicles are becoming popular as an
alternative of conventional fossil fuel vehicles. Conventional Internal
Combustion Engine (ICE) vehicle uses fossil fuel which contributing
a major part of overall carbon emission in the environment. Carbon
and other green house gas emission are responsible for global
warming and resulting climate change. It becomes vital to evaluate
performance of vehicle based on emission. In this paper an effort has
been made to depict the picture of emission caused by vehicle and
scenario of Australia has taken into account. Effort has been made to
compare the fossil based vehicle with electric vehicle in phases. The
study also evaluates advancement in electric vehicle technology,
required infrastructure for sustainability and future scope of
developments. This paper also includes the evaluation of electric
vehicle concept for pollution control and sustainable transport
systems in future. This study can be a benchmark for development of
electric vehicle as low carbon emission alternative for the cities of
tomorrow.
Abstract: The right to housing is a basic need while good
quality and affordable housing is a reflection of a high quality of life.
However, housing remains a major problem for most, especially for
the bottom billions. Satisfaction on housing and neighbourhood
conditions are one of the important indicators that reflect quality of
life. These indicators are also important in the process of evaluating
housing policy with the objective to increase the quality of housing
and neighbourhood. The research method is purely based on a
quantitative method, using a survey. The findings show that housing
purchasing trend in urban Malaysia is determined by demographic
profiles, mainly by education level, age, gender and income. The
period of housing ownership also influenced the socio-cultural
interactions and satisfaction of house owners with their
neighbourhoods. The findings also show that the main concerns for
house buyers in urban areas are price and location of the house.
Respondents feel that houses in urban Malaysia is too expensive and
beyond their affordability. Location of houses and distance from
work place are also regarded as the main concern. However,
respondents are fairly satisfied with religious and socio-cultural
facilities in the housing areas and most importantly not many regard
ethnicity as an issue in their decision-making, when buying a house.
Abstract: This paper proposes a new parameter identification
method based on Linear Fractional Transformation (LFT). It is
assumed that the target linear system includes unknown parameters.
The parameter deviations are separated from a nominal system via
LFT, and identified by organizing I/O signals around the separated
deviations of the real system. The purpose of this paper is to apply LFT
to simultaneously identify the parameter deviations in systems with
fewer outputs than unknown parameters. As a fundamental example,
this method is implemented to one degree of freedom vibratory system.
Via LFT, all physical parameters were simultaneously identified in this
system. Then, numerical simulations were conducted for this system to
verify the results. This study shows that all the physical parameters of a
system with fewer outputs than unknown parameters can be effectively
identified simultaneously using LFT.
Abstract: Fluids are used for heat transfer in many engineering
equipments. Water, ethylene glycol and propylene glycol are some
of the common heat transfer fluids. Over the years, in an attempt to
reduce the size of the equipment and/or efficiency of the process,
various techniques have been employed to improve the heat transfer
rate of these fluids. Surface modification, use of inserts and
increased fluid velocity are some examples of heat transfer
enhancement techniques. Addition of milli or micro sized particles
to the heat transfer fluid is another way of improving heat transfer
rate. Though this looks simple, this method has practical problems
such as high pressure loss, clogging and erosion of the material of
construction. These problems can be overcome by using nanofluids,
which is a dispersion of nanosized particles in a base fluid.
Nanoparticles increase the thermal conductivity of the base fluid
manifold which in turn increases the heat transfer rate. In this work,
the heat transfer enhancement using aluminium oxide nanofluid has
been studied by computational fluid dynamic modeling of the
nanofluid flow adopting the single phase approach.
Abstract: Mechanical interaction between endothelial cells (ECs) and the extracellular matrix (or collagen gel) is known to influence the sprouting response of endothelial cells during angiogenesis. This influence is believed to impact on the capability of endothelial cells to sense soluble chemical cues. Quantitative analysis of endothelial-cell-mediated displacement of the collagen gel provides a means to explore this mechanical interaction. Existing analysis in this context is generally limited to 2D settings. In this paper, we investigate the mechanical interaction between endothelial cells and the extracellular matrix in terms of the endothelial-cellmediated displacement of the collagen gel in both 2D and 3D. Digital image correlation and Digital volume correlation are applied on confocal reflectance image stacks to analyze cell-mediated displacement of the gel. The skeleton of the sprout is extracted from phase contrast images and superimposed on the displacement field to further investigate the link between the development of the sprout and the displacement of the gel.
Abstract: In this paper, a new K-means clustering based
approach for identification of voltage control areas is developed.
Voltage control areas are important for efficient reactive power
management in power systems operating under deregulated
environment. Although, voltage control areas are formed using
conventional hierarchical clustering based method, but the present
paper investigate the capability of K-means clustering for the
purpose of forming voltage control areas. The proposed method is
tested and compared for IEEE 14 bus and IEEE 30 bus systems. The
results show that this K-means based method is competing with
conventional hierarchical approach
Abstract: Topical photodynamic therapy (PDT) with
5-aminolevulinic acid (ALA) is an alternative therapy for treating
superficial cancer, especially for skin or oral cancer. ALA, a precursor
of the photosensitizer protoporphyrin IX (PpIX), is present as
zwitterions and hydrophilic property which make the low permeability
through the cell membrane. Collagen is a traditional carrier; its
molecular composed various amino acids which bear positive charge
and negative charge. In order to utilize the ion-pairs with ALA and
collagen, the study employed various pH values adjusting the net
charge. The aim of this study was to compare a series collagen form,
including solution, gel and sponge to investigate the topical delivery
behavior of ALA. The in vivo confocal laser scanning microscopy
(CLSM) study demonstrated that PpIX generation ability was different
pattern after apply for 6 h. Gel type could generate high PpIX, and
archived more deep of skin depth.
Abstract: Methods to detect and localize time singularities of polynomial and quasi-polynomial ordinary differential equations are systematically presented and developed. They are applied to examples taken form different fields of applications and they are also compared to better known methods such as those based on the existence of linear first integrals or Lyapunov functions.
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
Abstract: Multi-agent system is composed by several agents
capable of reaching the goal cooperatively. The system needs an agent
platform for efficient and stable interaction between intelligent agents.
In this paper we propose a flexible and scalable agent platform by
composing the containers with multiple hierarchical agent groups. It
also allows efficient implementation of multiple domain presentations
of the agents unlike JADE. The proposed platform provides both
group management and individual management of agents for
efficiency. The platform has been implemented and tested, and it can
be used as a flexible foundation of the dynamic multi-agent system
targeting seamless delivery of ubiquitous services.
Abstract: Timetabling problems are often hard and timeconsuming
to solve. Most of the methods of solving them concern
only one problem instance or class. This paper describes a universal
method for solving large, highly constrained timetabling problems
from different domains. The solution is based on evolutionary
algorithm-s framework and operates on two levels – first-level
evolutionary algorithm tries to find a solution basing on given set of
operating parameters, second-level algorithm is used to establish
those parameters. Tabu search is employed to speed up the solution
finding process on first level. The method has been used to solve
three different timetabling problems with promising results.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.
Abstract: In the present simulation work, an attempt is made to study the switching dynamics of an optically controlled 4HSiC thyristor power semiconductor device with the use of GaAs optically triggered power transistor. The half-cell thyristor has the forward breakdown of 200 V and reverse breakdown of more than 1000 V. The optically controlled thyristor has a rise time of 0.14 μs and fall time of 0.065 μs. The turn-on and turn-off delays are 0.1 μs and 0.06 μs, respectively. In addition, this optically controlled thyristor is used as a control switch for the DC-DC Boost converter. The pn-diode used for the converter has the forward drop of 2.8 V and reverse breakdown of around 400 V.
Abstract: Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.