Abstract: The issue of unintentional islanding in PV grid
interconnection still remains as a challenge in grid-connected
photovoltaic (PV) systems. This paper discusses the overview of
popularly used anti-islanding detection methods, practically applied
in PV grid-connected systems. Anti-islanding methods generally can
be classified into four major groups, which include passive methods,
active methods, hybrid methods and communication base methods.
Active methods have been the preferred detection technique over the
years due to very small non-detected zone (NDZ) in small scale
distribution generation. Passive method is comparatively simpler
than active method in terms of circuitry and operations. However, it
suffers from large NDZ that significantly reduces its performance.
Communication base methods inherit the advantages of active and
passive methods with reduced drawbacks. Hybrid method which
evolved from the combination of both active and passive methods
has been proven to achieve accurate anti-islanding detection by many
researchers. For each of the studied anti-islanding methods, the
operation analysis is described while the advantages and
disadvantages are compared and discussed. It is difficult to pinpoint a
generic method for a specific application, because most of the
methods discussed are governed by the nature of application and
system dependent elements. This study concludes that the setup and
operation cost is the vital factor for anti-islanding method selection in
order to achieve minimal compromising between cost and system
quality.
Abstract: This paper proposes a method, combining color and
layout features, for identifying documents captured from lowresolution
handheld devices. On one hand, the document image color
density surface is estimated and represented with an equivalent
ellipse and on the other hand, the document shallow layout structure
is computed and hierarchically represented. The combined color and
layout features are arranged in a symbolic file, which is unique for
each document and is called the document-s visual signature. Our
identification method first uses the color information in the
signatures in order to focus the search space on documents having a
similar color distribution, and finally selects the document having the
most similar layout structure in the remaining search space. Finally,
our experiment considers slide documents, which are often captured
using handheld devices.
Abstract: This study reveals that anti-immigrant policies in
Europe result from a process of securitization, and that, within this
process, radical right parties have been formulating discourses and
approaches through a construction process by using some common
security themes. These security themes can be classified as national
security, economic security, cultural security and internal security.
The frequency with which radical right parties use these themes may
vary according to the specific historical, social and cultural
characteristics of a particular country.
Abstract: This paper presents a simplified version of Data Envelopment Analysis (DEA) - a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object - the one having greatest outputs and smallest inputs. It allows for obtaining an explicit analytical solution and making a step to an absolute efficiency. This paper develops this approach further and introduces a DEA model with Partially Perfect Objects. DEA PPO consecutively eliminates the smallest relative inputs or greatest relative outputs, and applies DEA PO to the reduced collections of indicators. The partial efficiency scores are combined to get the weighted efficiency score. The computational scheme remains simple, like that of DEA PO, but the advantage of the DEA PPO is taking into account all of the inputs and outputs for each actual object. Firm evaluation is considered as an example.
Abstract: This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit 'one' and the un-pointed letters with extension to hold 'zero'. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu.
Abstract: A conventional binding method for low power in a
high-level synthesis mainly focuses on finding an optimal binding for
an assumed input data, and obtains only one binding table. In this
paper, we show that a binding method which uses multiple binding
tables gets better solution compared with the conventional methods
which use a single binding table, and propose a dynamic bus binding
scheme for low power using multiple binding tables. The proposed
method finds multiple binding tables for the proper partitions of an
input data, and switches binding tables dynamically to produce the
minimum total switching activity. Experimental result shows that the
proposed method obtains a binding solution having 12.6-28.9%
smaller total switching activity compared with the conventional
methods.
Abstract: Blood pulse is an important human physiological signal commonly used for the understanding of the individual physical health. Current methods of non-invasive blood pulse sensing require direct contact or access to the human skin. As such, the performances of these devices tend to vary with time and are subjective to human body fluids (e.g. blood, perspiration and skin-oil) and environmental contaminants (e.g. mud, water, etc). This paper proposes a simulation model for the novel method of non-invasive acquisition of blood pulse using the disturbance created by blood flowing through a localized magnetic field. The simulation model geometry represents a blood vessel, a permanent magnet, a magnetic sensor, surrounding tissues and air in 2-dimensional. In this model, the velocity and pressure fields in the blood stream are described based on Navier-Stroke equations and the walls of the blood vessel are assumed to have no-slip condition. The blood assumes a parabolic profile considering a laminar flow for blood in major artery near the skin. And the inlet velocity follows a sinusoidal equation. This will allow the computational software to compute the interactions between the magnetic vector potential generated by the permanent magnet and the magnetic nanoparticles in the blood. These interactions are simulated based on Maxwell equations at the location where the magnetic sensor is placed. The simulated magnetic field at the sensor location is found to assume similar sinusoidal waveform characteristics as the inlet velocity of the blood. The amplitude of the simulated waveforms at the sensor location are compared with physical measurements on human subjects and found to be highly correlated.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: Sharing the manufacturing facility through remote
operation and monitoring of a machining process is challenge for
effective use the production facility. Several automation tools in term
of hardware and software are necessary for successfully remote
operation of a machine. This paper presents a prototype of workpiece
holding attachment for remote operation of milling process by self
configuration the workpiece setup. The prototype is designed with
mechanism to reorient the work surface into machining spindle
direction with high positioning accuracy. Variety of parts geometry
is hold by attachment to perform single setup machining. Pin type
with array pattern additionally clamps the workpiece surface from
two opposite directions for increasing the machining rigidity.
Optimum pins configuration for conforming the workpiece geometry
with minimum deformation is determined through hybrid algorithms,
Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).
Prototype with intelligent optimization technique enables to hold
several variety of workpiece geometry which is suitable for
machining low of repetitive production in remote operation.
Abstract: Electrospinning is a broadly used technology to obtain
polymeric nanofibers ranging from several micrometers down to
several hundred nanometers for a wide range of applications. It offers
unique capabilities to produce nanofibers with controllable porous
structure. With smaller pores and higher surface area than regular
fibers, electrospun fibers have been successfully applied in various
fields, such as, nanocatalysis, tissue engineering scaffolds, protective
clothing, filtration, biomedical, pharmaceutical, optical electronics,
healthcare, biotechnology, defense and security, and environmental
engineering. In this study, polyurethane nanofibers were obtained
under different electrospinning parameters. Fiber morphology and
diameter distribution were investigated in order to understand them
as a function of process parameters.
Abstract: This paper reports a case study on how a conceptual
and analytical thinking approach was used in Art and Design Department at Multimedia University (Malaysia) in addressing the
issues of one nation and its impact in the society through artworks. The art project was designed for students to increase the know-how
and develop creative thinking in design and communication. Goals of the design project were: (1) to develop creative thinking in design
and communication, (2) to increase student understanding on the
process of problem solving for design work, and (3) to use design
elements and principles to generate interest, attention and emotional responses. An exhibition entitled "One Nation" was showcased to
local and international viewers consisting of the general public, professionals, academics, artists and students. Findings indicate that the project supported several visual art standards, as well as
generated awareness in the society. This project may be of interest to
current and future art educators and others interested in the potential
of utilizing global issues as content for art, community and environment studies for the purpose of educational art.
Abstract: This paper introduces a temporal epistemic logic
CBCTL that updates agent-s belief states through communications
in them, based on computational tree logic (CTL). In practical
environments, communication channels between agents may not be
secure, and in bad cases agents might suffer blackouts. In this study,
we provide inform* protocol based on ACL of FIPA, and declare the
presence of secure channels between two agents, dependent on time.
Thus, the belief state of each agent is updated along with the progress
of time. We show a prover, that is a reasoning system for a given
formula in a given a situation of an agent ; if it is directly provable
or if it could be validated through the chains of communications, the
system returns the proof.
Abstract: Software Development Risks Identification (SDRI),
using Fault Tree Analysis (FTA), is a proposed technique to identify
not only the risk factors but also the causes of the appearance of the
risk factors in software development life cycle. The method is based
on analyzing the probable causes of software development failures
before they become problems and adversely affect a project. It uses
Fault tree analysis (FTA) to determine the probability of a particular
system level failures that are defined by A Taxonomy for Sources of
Software Development Risk to deduce failure analysis in which an
undesired state of a system by using Boolean logic to combine a
series of lower-level events. The major purpose of this paper is to use
the probabilistic calculations of Fault Tree Analysis approach to
determine all possible causes that lead to software development risk
occurrence
Abstract: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: Software estimation accuracy is among the greatest
challenges for software developers. This study aimed at building and
evaluating a neuro-fuzzy model to estimate software projects
development time. The forty-one modules developed from ten
programs were used as dataset. Our proposed approach is compared
with fuzzy logic and neural network model and Results show that the
value of MMRE (Mean of Magnitude of Relative Error) applying
neuro-fuzzy was substantially lower than MMRE applying fuzzy
logic and neural network.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: In this paper we compare the response of linear and
nonlinear neural network-based prediction schemes in prediction of
received Signal-to-Interference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DS/CDMA) systems. The
nonlinear predictor is Multilayer Perceptron MLP and the linear
predictor is an Adaptive Linear (Adaline) predictor. We solve the
problem of complexity by using the Minimum Mean Squared Error
(MMSE) principle to select the optimal predictors. The optimized
Adaline predictor is compared to optimized MLP by employing
noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an
urban environment. The results show that the Adaline predictor can
estimates SIR with the same error as MLP when the user has the
velocity of 5 km/h and 60 km/h but by increasing the velocity up-to
120 km/h the mean squared error of MLP is two times more than
Adaline predictor. This makes the Adaline predictor (with lower
complexity) more suitable than MLP for closed-loop power control
where efficient and accurate identification of the time-varying
inverse dynamics of the multi path fading channel is required.
Abstract: Supplier selection is a multi criteria decision-making process that comprises tangible and intangible factors. The majority of previous supplier selection techniques do not consider strategic perspective. Besides, uncertainty is one of the most important obstacles in supplier selection. For the first, time in this paper, the idea of the algorithm " Knapsack " is used to select suppliers Moreover, an attempt has to be made to take the advantage of a simple numerical method for solving model .This is an innovation to resolve any ambiguity in choosing suppliers. This model has been tried in the suppliers selected in a competitive environment and according to all desired standards of quality and quantity to show the efficiency of the model, an industry sample has been uses.
Abstract: Reconfigurable optical add/drop multiplexers
(ROADMs) can be classified into three categories based on their
underlying switching technologies. Category I consists of a single
large optical switch; category II is composed of a number of small
optical switches aligned in parallel; and category III has a single
optical switch and only one wavelength being added/dropped. In this
paper, to evaluate the wavelength-routing capability of ROADMs of
category-II in dynamic optical networks,the dynamic traffic models
are designed based on Bernoulli, Poisson distributions for smooth
and regular types of traffic. Through Analytical and Simulation
results, the routing power of cat-II of ROADM networks for two
traffic models are determined.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process