Abstract: In development of floating photovoltaic generation
system, finding a suitable place of installation is as important as
development of economically feasible and stable structure. Especially
since floating photovoltaic system has its facility floating on water
surface, it is extremely important to review the effects of weather
conditions such as wind, water flow and floating matters, various
factors (such as fogs) that can reduce generation efficiency, possibility
of connection with power system, and legal restrictions. The method of
investigating suitable area and resource for development of
tracking-type floating photovoltaic generation system was proposed in
this paper, which can be used for development of floating and ocean
photovoltaic system in the future.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: Anultra-low power capacitor less low-dropout voltage
regulator with improved transient response using gain enhanced feed
forward path compensation is presented in this paper. It is based on a
cascade of a voltage amplifier and a transconductor stage in the feed
forward path with regular error amplifier to form a composite gainenhanced
feed forward stage. It broadens the gain bandwidth and thus
improves the transient response without substantial increase in power
consumption. The proposed LDO, designed for a maximum output
current of 100 mA in UMC 180 nm, requires a quiescent current of
69 )A. An undershot of 153.79mV for a load current changes from
0mA to 100mA and an overshoot of 196.24mV for current change of
100mA to 0mA. The settling time is approximately 1.1 )s for the
output voltage undershooting case. The load regulation is of 2.77
)V/mA at load current of 100mA. Reference voltage is generated by
using an accurate band gap reference circuit of 0.8V.The costly
features of SOC such as total chip area and power consumption is
drastically reduced by the use of only a total compensation
capacitance of 6pF while consuming power consumption of 0.096
mW.
Abstract: Composite materials have important assets compared
to traditional materials. They bring many functional advantages:
lightness, mechanical resistance and chemical, etc. In the present
study we examine the effect of a circular central notch and a precrack
on the tensile fracture of two woven composite materials. The tensile
tests were applied to a standardized specimen, notched and a
precarcked (orientation of the crack 0°, 45° and 90°). These tensile
tests were elaborated according to an experimental planning design of
the type 23.31 requiring 24 experiments with three repetitions. By the
analysis of regression, we obtained a mathematical model describing
the maximum load according to the influential parameters (hole
diameter, precrack length, angle of a precrack orientation). The
specimens precracked at 90° have a better behavior than those having
a precrack at 45° and still better than those having of the precracks
oriented at 0°. In addition the maximum load is inversely
proportional to the notch size.
Abstract: The paper describes the experiments and the kinetic
parameters calculus of the gasoil hydrofining. They are presented
experimental results of gasoil hidrofining using Mo and promoted
with Ni on aluminum support catalyst. The authors have adapted a
kinetic model gasoil hydrofining. Using this proposed kinetic model
and the experimental data they have calculated the parameters of the
model. The numerical calculus is based on minimizing the difference
between the experimental sulf concentration and kinetic model
estimation.
Abstract: This research proposes a novel reconstruction protocol
for restoring missing surfaces and low-quality edges and shapes in
photos of artifacts at historical sites. The protocol starts with the
extraction of a cloud of points. This extraction process is based on
four subordinate algorithms, which differ in the robustness and
amount of resultant. Moreover, they use different -but
complementary- accuracy to some related features and to the way
they build a quality mesh. The performance of our proposed protocol
is compared with other state-of-the-art algorithms and toolkits. The
statistical analysis shows that our algorithm significantly outperforms
its rivals in the resultant quality of its object files used to reconstruct
the desired model.
Abstract: In this paper we make a temperature investigations in
two type of superposed crimped connections using experimental
determinations. All the samples use 8 copper wire 7.1 x 3 mm2
crimped by two methods: the first method uses one crimp indents and
the second is a proposed method with two crimp indents. The ferrule
is a parallel one. We study the influence of number and position of
crimp indents. The samples are heated in A.C. current at different
current values until steady state heating regime. After obtaining of
temperature values, we compare them and present the conclusion.
Abstract: Color Histogram is considered as the oldest method
used by CBIR systems for indexing images. In turn, the global
histograms do not include the spatial information; this is why the
other techniques coming later have attempted to encounter this
limitation by involving the segmentation task as a preprocessing step.
The weak segmentation is employed by the local histograms while
other methods as CCV (Color Coherent Vector) are based on strong
segmentation. The indexation based on local histograms consists of
splitting the image into N overlapping blocks or sub-regions, and
then the histogram of each block is computed. The dissimilarity
between two images is reduced, as consequence, to compute the
distance between the N local histograms of the both images resulting
then in N*N values; generally, the lowest value is taken into account
to rank images, that means that the lowest value is that which helps to
designate which sub-region utilized to index images of the collection
being asked. In this paper, we make under light the local histogram
indexation method in the hope to compare the results obtained against
those given by the global histogram. We address also another
noteworthy issue when Relying on local histograms namely which
value, among N*N values, to trust on when comparing images, in
other words, which sub-region among the N*N sub-regions on which
we base to index images. Based on the results achieved here, it seems
that relying on the local histograms, which needs to pose an extra
overhead on the system by involving another preprocessing step
naming segmentation, does not necessary mean that it produces better
results. In addition to that, we have proposed here some ideas to
select the local histogram on which we rely on to encode the image
rather than relying on the local histogram having lowest distance with
the query histograms.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: This paper presents a 3D guidance scheme for
Unmanned Aerial Vehicles (UAVs). The proposed guidance scheme
is based on the sliding mode approach using nonlinear sliding
manifolds. Generalized 3D kinematic equations are considered
here during the design process to cater for the coupling between
longitudinal and lateral motions. Sliding mode based guidance
scheme is then derived for the multiple-input multiple-output
(MIMO) system using the proposed nonlinear manifolds. Instead of
traditional sliding surfaces, nonlinear sliding surfaces are proposed
here for performance and stability in all flight conditions. In the
reaching phase control inputs, the bang-bang terms with signum
functions are accompanied with proportional terms in order to reduce
the chattering amplitudes. The Proposed 3D guidance scheme is
implemented on a 6-degrees-of-freedom (6-dof) simulation of a UAV
and simulation results are presented here for different 3D trajectories
with and without disturbances.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: The importance of the formal specification in the
software life cycle is barely concealing to anyone. Formal
specifications use mathematical notation to describe the properties of
information system precisely, without unduly constraining the way in
how these properties are achieved. Having a correct and quality
software specification is not easy task. This study concerns with how
a group of rectifiers can communicate with each other and work to
prepare and produce a correct formal software specification. WBCS
has been implemented based mainly in the proposed supported
cooperative work model and a survey conducted on the existing Webbased
collaborative writing tools. This paper aims to assess the
feasibility of executing the web-based collaboration process using
WBCS. The purpose of conducting this test is to test the system as a
whole for functionality and fitness for use based on the evaluation
test plan.
Abstract: Among all FACTS devices, the unified power flow
controller (UPFC) is considered to be the most versatile device.
This is due to its capability to control all the transmission system
parameters (impedance, voltage magnitude, and phase angle). With
the growing interest in UPFC, the attention to develop a mathematical
model has increased. Several models were introduced for UPFC in
literature for different type of studies in power systems. In this paper
a novel comparison study between two dynamic models of UPFC
with their proposed control strategies.
Abstract: This work proposes a fuzzy methodology to support
the investment decisions. While choosing among competitive
investment projects, the methodology makes ranking of projects
using the new aggregation OWA operator – AsPOWA, presented in
the environment of possibility uncertainty. For numerical evaluation
of the weighting vector associated with the AsPOWA operator the
mathematical programming problem is constructed. On the basis of
the AsPOWA operator the projects’ group ranking maximum criteria
is constructed. The methodology also allows making the most
profitable investments into several of the project using the method
developed by the authors for discrete possibilistic bicriteria problems.
The article provides an example of the investment decision-making
that explains the work of the proposed methodology.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: Natural antimicrobials are used to preserve foods that
can be found in plants, animals, and microorganisms. Antimicrobial
substances are natural or artificial agents that produced by
microorganisms or obtained semi/total chemical synthesis are used at
low concentrations to inhibit the growth of other microorganisms.
Food borne pathogens and spoilage microorganisms are inactivated
by the use of antagonistic microorganisms and their metabolites.
Yeasts can produce toxic proteins or glycoproteins (toxins) that cause
inhibition of sensitive bacteria and yeast species. Antimicrobial
substance producing phenotypes belonging different yeast genus
were isolated from different sources. Toxins secreted by many yeast
strains inhibiting the growth of other yeast strains. These strains show
antimicrobial activity, inhibiting the growth of mold and bacteria.
The effect of antimicrobial agents produced by yeasts can be
extremely fast, and therefore may be used in various treatment
procedures. Rapid inhibition of microorganisms is possibly caused by
microbial cell membrane lipopolysaccharide binding and in
activation (neutralization) effect. Antimicrobial agents inhibit the
target cells via different mechanisms of action.
Abstract: The work proposes a decision support methodology
for the credit risk minimization in selection of investment projects.
The methodology provides two stages of projects’ evaluation.
Preliminary selection of projects with minor credit risks is made
using the Expertons Method. The second stage makes ranking of
chosen projects using the Possibilistic Discrimination Analysis
Method. The latter is a new modification of a well-known Method of
Fuzzy Discrimination Analysis.
Abstract: A generalized vortex lattice method for complex
lifting surfaces with flap and aileron deflection is formulated. The
method is not restricted by the linearized theory assumption and
accounts for all standard geometric lifting surface parameters:
camber, taper, sweep, washout, dihedral, in addition to flap and
aileron deflection. Thickness is not accounted for since the physical
lifting body is replaced by a lattice of panels located on the mean
camber surface. This panel lattice setup and the treatment of different
wake geometries is what distinguish the present work form the
overwhelming majority of previous solutions based on the vortex
lattice method. A MATLAB code implementing the proposed
formulation is developed and validated by comparing our results to
existing experimental and numerical ones and good agreement is
demonstrated. It is then used to study the accuracy of the widely used
classical vortex-lattice method. It is shown that the classical approach
gives good agreement in the clean configuration but is off by as much
as 30% when a flap or aileron deflection of 30° is imposed. This
discrepancy is mainly due the linearized theory assumption
associated with the conventional method. A comparison of the effect
of four different wake geometries on the values of aerodynamic
coefficients was also carried out and it is found that the choice of the
wake shape had very little effect on the results.
Abstract: Sudoku is a logic-based combinatorial puzzle game
which people in different ages enjoy playing it. The challenging and
addictive nature of this game has made it a ubiquitous game. Most
magazines, newspapers, puzzle books, etc. publish lots of Sudoku
puzzles every day. These puzzles often come in different levels of
difficulty so that all people, from beginner to expert, can play the
game and enjoy it. Generating puzzles with different levels of
difficulty is a major concern of Sudoku designers. There are several
works in the literature which propose ways of generating puzzles
having a desirable level of difficulty. In this paper, we propose a
method based on constraint satisfaction problems to evaluate the
difficulty of the Sudoku puzzles. Then we propose a hill climbing
method to generate puzzles with different levels of difficulty.
Whereas other methods are usually capable of generating puzzles
with only few number of difficulty levels, our method can be used to
generate puzzles with arbitrary number of different difficulty levels.
We test our method by generating puzzles with different levels of
difficulty and having a group of 15 people solve all the puzzles and
recording the time they spend for each puzzle.
Abstract: A method is proposed for stable detection of
seismoacoustic sources in C-OTDR systems that guarantee given
upper bounds for probabilities of type I and type II errors. Properties
of the proposed method are rigorously proved. The results of
practical applications of the proposed method in a real C-OTDRsystem
are presented.