Abstract: This paper presents a model predictive control (MPC)
of a utility interactive three phase inverter (TPI) for a photovoltaic
(PV) system at commercial level. The proposed model uses phase
locked loop (PLL) to synchronize the TPI with the power electric
grid (PEG) and performs MPC control in a dq reference frame. TPI
model consists of a boost converter (BC), maximum power point
tracking (MPPT) control, and a three-leg voltage source inverter
(VSI). The operational model of VSI is used to synthesize the
sinusoidal current and track the reference. The model is validated
using a 35.7 kW PV system in Matlab/Simulink. Implementation
results show simplicity and accuracy, as well as reliability of the
model.
Abstract: The Greater Athens Area (GAA) faces photochemical
and particulate pollution episodes as a result of the combined effects
of local pollutant emissions, regional pollution transport, synoptic
circulation and topographic characteristics. The area has undergone
significant changes since the Athens 2004 Olympic Games because
of large scale infrastructure works that lead to the shift of population
to areas previously characterized as rural, the increase of the traffic
fleet and the operation of highways. However, few recent modelling
studies have been performed due to the lack of an accurate, updated
emission inventory. The photochemical modelling system
MM5/CAMx was applied in order to study the photochemical and
particulate pollution characteristics above the GAA for two distinct
ten-day periods in the summer of 2006 and 2010, where air pollution
episodes occurred. A new updated emission inventory was used
based on official data. Comparison of modeled results with
measurements revealed the importance and accuracy of the new
Athens emission inventory as compared to previous modeling
studies. The model managed to reproduce the local meteorological
conditions, the daily ozone and particulates fluctuations at different
locations across the GAA. Higher ozone levels were found at
suburban and rural areas as well as over the sea at the south of the
basin. Concerning PM10, high concentrations were computed at the
city centre and the southeastern suburbs in agreement with measured
data. Source apportionment analysis showed that different sources
contribute to the ozone levels, the local sources (traffic, port
activities) affecting its formation.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: This study presents a kinematic positioning approach
that uses a global positioning system (GPS) buoy for precise ocean
surface monitoring. The GPS buoy data from the two experiments are
processed using an accurate, medium-range differential kinematic
technique. In each case, the data from a nearby coastal site are
collected at a high rate (1 Hz) for more than 24 hours, and
measurements are conducted in neighboring tidal stations to verify
the estimated sea surface heights. The GPS buoy kinematic
coordinates are estimated using epoch-wise pre-elimination and a
backward substitution algorithm. Test results show that centimeterlevel
accuracy can be successfully achieved in determining sea
surface height using the proposed technique. The centimeter-level
agreement between the two methods also suggests the possibility of
using this inexpensive and more flexible GPS buoy equipment to
enhance (or even replace) current tidal gauge stations.
Abstract: A large variety of pipe flange is required in marine
and construction industry. Pipe flanges are usually welded or screwed
to the pipe end and are connected with bolts. This approach is very
simple and widely used for a long time; however, it results in high
development cost and low productivity, and the productions made by
this approach usually have safety problem at the welding area. In this
research, a new approach of forming pipe flange based on cold
forging and floating die concept is presented. This innovative
approach increases the effectiveness of the material usage and save
the time cost compared with conventional welding method. To ensure the dimensional accuracy of the final product, the finite
element analysis (FEA) was carried out to simulate the process of
cold forging, and the orthogonal experiment methods were used to
investigate the influence of four manufacturing factors (pin die angle,
pipe flange angle, rpm, pin die distance from clamp jig) and predicted
the best combination of them. The manufacturing factors were
obtained by numerical and experimental studies and it shows that the
approach is very useful and effective for the forming of pipe flange,
and can be widely used later.
Abstract: Recently, numerous documents including large
volumes of unstructured data and text have been created because of the
rapid increase in the use of social media and the Internet. Usually,
these documents are categorized for the convenience of users. Because
the accuracy of manual categorization is not guaranteed, and such
categorization requires a large amount of time and incurs huge costs.
Many studies on automatic categorization have been conducted to help
mitigate the limitations of manual categorization. Unfortunately, most
of these methods cannot be applied to categorize complex documents
with multiple topics because they work on the assumption that
individual documents can be categorized into single categories only.
Therefore, to overcome this limitation, some studies have attempted to
categorize each document into multiple categories. However, the
learning process employed in these studies involves training using a
multi-categorized document set. These methods therefore cannot be
applied to the multi-categorization of most documents unless
multi-categorized training sets using traditional multi-categorization
algorithms are provided. To overcome this limitation, in this study, we
review our novel methodology for extending the category of a
single-categorized document to multiple categorizes, and then
introduce a survey-based verification scenario for estimating the
accuracy of our automatic categorization methodology.
Abstract: In this paper the influence of errors of function derivatives in initial time which have been obtained by experiment (uncontrollable inaccuracy) to the results of inverse problem solution was investigated. It was shown that these errors distort the inverse problem solution as a rule near the beginning of interval where the solutions are analyzed. Several methods for removing the influence of uncontrollable inaccuracy have been suggested.
Abstract: In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.
Abstract: Current transformers are an integral part of power
system because it provides a proportional safe amount of current for
protection and measurement applications. However, when the power
system experiences an abnormal situation leading to huge current
flow, then this huge current is proportionally injected to the
protection and metering circuit. Since the protection and metering
equipment’s are designed to withstand only certain amount of current
with respect to time, these high currents pose a risk to man and
equipment. Therefore, during such instances, the CT saturation
characteristics have a huge influence on the safety of both man and
equipment and on the reliability of the protection and metering
system. This paper shows the effect of burden on the Accuracy Limiting
factor/ Instrument security factor of current transformers and the
change in saturation characteristics of the CT’s. The response of the
CT to varying levels of overcurrent at different connected burden will
be captured using the data acquisition software LabVIEW. Analysis
is done on the real time data gathered using LabVIEW. Variation of
current transformer saturation characteristics with changes in burden
will be discussed.
Abstract: To decrease the grating scale thermal expansion error,
a novel method which based on multiple temperature detection is
proposed. Several temperature sensors are installed on the grating
scale and the temperatures of these sensors are recorded. The
temperatures of every point on the grating scale are calculated by
interpolating between adjacent sensors. According to the thermal
expansion principle, the grating scale thermal expansion error model
can be established by doing the integral for the variations of position
and temperature. A novel compensation method is proposed in this
paper. By applying the established error model, the grating scale
thermal expansion error is decreased by 90% compared with no
compensation. The residual positioning error of the grating scale is
less than 15μm/10m and the accuracy of the machine tool is
significant improved.
Abstract: Wire Electric Discharge Machining (WEDM) is
thermal machining process capable of machining very hard
electrically conductive material irrespective of their hardness.
WEDM is being widely used to machine micro scale parts with the
high dimensional accuracy and surface finish. The objective of this
paper is to optimize the process parameters of wire EDM to fabricate
the micro channels and to calculate the surface finish and material
removal rate of micro channels fabricated using wire EDM. The
material used is aluminum 6061 alloy. The experiments were
performed using CNC wire cut electric discharge machine. The effect
of various parameters of WEDM like pulse on time (TON) with the
levels (100, 150, 200), pulse off time (TOFF) with the levels (25, 35,
45) and current (IP) with the levels (105, 110, 115) were investigated
to study the effect on output parameter i.e. Surface Roughness and
Material Removal Rate (MRR). Each experiment was conducted
under different conditions of pulse on time, pulse off time and peak
current. For material removal rate, TON and Ip
were the most significant process parameter. MRR increases with the increase in
TON and Ip and decreases with the increase in TOFF. For surface
roughness, TON and Ip have the maximum effect and TOFF was found
out to be less effective.
Abstract: This paper suggests a new internal architecture of
holon based on feature selection model using the combination of
Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is
used to generate features while ANN is used as a classifier to
evaluate the produced features. Proposed system is applied on the
Wine dataset, the statistical result proves that the proposed system is
effective and has the ability to choose informative features with high
accuracy.
Abstract: A new approach has been developed to estimate the
load share and distribution of worm gear drives, and to calculate the
instantaneous tooth meshing stiffness. In the approach, the worm gear
drive was modelled as a series of spur gear slices, and each slice was
analyzed separately using the well-established formulae of spur gear
loading and stresses. By combining the results obtained for all slices,
the entire envolute worm gear set loading and stressing was obtained. The geometric modelling method presented allows tooth elastic
deformation and tooth root stresses of worm gear drives under
different load conditions to be investigated. Based on the slicing
method introduced in this study, the instantaneous meshing stiffness
and load share are obtained. In comparison with existing methods,
this approach has both good analysis accuracy and less computing
time.
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Abstract: The present paper attempts to investigate the
prediction of air entrainment rate and aeration efficiency of a free
overfall jets issuing from a triangular sharp crested weir by using
regression based modelling. The empirical equations, Support vector
machine (polynomial and radial basis function) models and the linear
regression techniques were applied on the triangular sharp crested
weirs relating the air entrainment rate and the aeration efficiency to
the input parameters namely drop height, discharge, and vertex angle.
It was observed that there exists a good agreement between the
measured values and the values obtained using empirical equations,
Support vector machine (Polynomial and rbf) models and the linear
regression techniques. The test results demonstrated that the SVM
based (Poly & rbf) model also provided acceptable prediction of the
measured values with reasonable accuracy along with empirical
equations and linear regression techniques in modelling the air
entrainment rate and the aeration efficiency of a free overfall jets
issuing from triangular sharp crested weir. Further sensitivity analysis
has also been performed to study the impact of input parameter on the
output in terms of air entrainment rate and aeration efficiency.
Abstract: In this study, an Artificial Neural Network (ANN)
analytical method has been developed for analyzing earthquake
performances of the Reinforced Concrete (RC) buildings. 66 RC
buildings with four to ten storeys were subjected to performance
analysis according to the parameters which are the existing material,
loading and geometrical characteristics of the buildings. The selected
parameters have been thought to be effective on the performance of
RC buildings. In the performance analyses stage of the study, level of
performance possible to be shown by these buildings in case of an
earthquake was determined on the basis of the 4-grade performance
levels specified in Turkish Earthquake Code-2007 (TEC-2007). After
obtaining the 4-grade performance level, selected 23 parameters of
each building have been matched with the performance level. In this
stage, ANN-based fast evaluation algorithm mentioned above made
an economic and rapid evaluation of four to ten storey RC buildings.
According to the study, the prediction accuracy of ANN has been
found about 74%.
Abstract: The paper presents an advanced control system for
tennis ball throwing machines to improve their accuracy according to
the ball impact points. A further advantage of the system is the much
easier calibration process involving the intelligent solution of the
automatic adjustment of the stroking parameters according to the ball
elasticity, the self-calibration, the use of the safety margin at very flat
strokes and the possibility to placing the machine to any position of
the half court. The system applies mathematical methods to
determine the exact ball trajectories and special approximating
processes to access all points on the aimed half court.
Abstract: The localization information is crucial for the
operation of WSN. There are principally two types of localization
algorithms. The Range-based localization algorithm has strict
requirements on hardware, thus is expensive to be implemented in
practice. The Range-free localization algorithm reduces the hardware
cost. However, it can only achieve high accuracy in ideal scenarios.
In this paper, we locate unknown nodes by incorporating the
advantages of these two types of methods. The proposed algorithm
makes the unknown nodes select the nearest anchor using the
Received Signal Strength Indicator (RSSI) and choose two other
anchors which are the most accurate to achieve the estimated
location. Our algorithm improves the localization accuracy compared
with previous algorithms, which has been demonstrated by the
simulating results.
Abstract: The Multiple Intelligences theory characterizes human
intelligence as a multifaceted entity that exists in all human beings
with varying degrees. The most important contribution of this theory
to the field of English Language Teaching (ELT) is its role in
identifying individual differences and designing more learnercentered
programs. The present study aims at investigating the
relationship between different elements of multiple intelligence and
grammar scores. To this end, 63 female Iranian EFL learner selected
from among intermediate students participated in the study. The
instruments employed were a Nelson English language test, Michigan
Grammar Test, and Teele Inventory for Multiple Intelligences
(TIMI). The results of Pearson Product-Moment Correlation revealed
a significant positive correlation between grammatical accuracy and
linguistic as well as interpersonal intelligence. The results of
Stepwise Multiple Regression indicated that linguistic intelligence
contributed to the prediction of grammatical accuracy.