Abstract: A new technique based on Pattern search optimization is proposed for estimating different solar cell parameters in this paper. The estimated parameters are the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor. The proposed approach is tested and validated using double diode model to show its potential. Performance of the developed approach is quite interesting which signifies its potential as a promising estimation tool.
Abstract: Repetitive systems stand for a kind of systems that
perform a simple task on a fixed pattern repetitively, which are
widely spread in industrial fields. Hence, many researchers have been
interested in those systems, especially in the field of iterative learning
control (ILC). In this paper, we propose a finite-horizon tracking
control scheme for linear time-varying repetitive systems with uncertain
initial conditions. The scheme is derived both analytically
and numerically for state-feedback systems and only numerically for
output-feedback systems. Then, it is extended to stable systems with
input constraints. All numerical schemes are developed in the forms
of linear matrix inequalities (LMIs). A distinguished feature of the
proposed scheme from the existing iterative learning control is that
the scheme guarantees the tracking performance exactly even under
uncertain initial conditions. The simulation results demonstrate the
good performance of the proposed scheme.
Abstract: In this work, the natural convection in a concentric
annulus between a cold outer inclined square enclosure and heated
inner circular cylinder is simulated for two-dimensional steady
state. The Boussinesq approximation was applied to model the
buoyancy-driven effect and the governing equations were solved
using the time marching approach staggered by body fitted
coordinates. The coordinate transformation from the physical
domain to the computational domain is set up by an analytical
expression. Numerical results for Rayleigh numbers 103 , 104 , 105
and 106, aspect ratios 1.5 , 3.0 and 4.5 for seven different
inclination angles for the outer square enclosure 0o , -30o
, -45o
,
-60o , -90o , -135o , -180o are presented as well. The computed flow
and temperature fields were demonstrated in the form of
streamlines, isotherms and Nusselt numbers variation. It is found
that both the aspect ratio and the Rayleigh number are critical to the
patterns of flow and thermal fields. At all Rayleigh numbers angle
of inclination has nominal effect on heat transfer.
Abstract: This paper presents a new approach for busbar protection with stable operation of current transformer during saturation, using fuzzy neuro and symmetrical components theory. This technique uses symmetrical components of current signals to learn the hidden relationship existing in the input patterns. Simulation studies are preformed and the influence of changing system parameters such as inception fault and source impedance is studied. Details of the design procedure and the results of performance studies with the proposed relay are given in the paper. An analysis of the performance of the proposed technique during ct saturation conditions is presented. The performance of the technique was investigated for a variety of operating conditions and for several busbar configurations. Data generated by EMTDC simulations of model power systems were used in the investigations. The results indicate that the proposed technique is stable during ct saturation conditions.
Abstract: The Muslim faith requires individuals to fast between
the hours of sunrise and sunset during the month of Ramadan. Our
recent work has concentrated on some of the changes that take place
during the daytime when fasting. A questionnaire was developed to
assess subjective estimates of physical, mental and social activities,
and fatigue. Four days were studied: in the weeks before and after
Ramadan (control days) and during the first and last weeks of
Ramadan (experimental days). On each of these four days, this
questionnaire was given several times during the daytime and once
after the fast had been broken and just before individuals retired at
night.
During Ramadan, daytime mental, physical and social activities
all decreased below control values but then increased to abovecontrol
values in the evening. The desires to perform physical and
mental activities showed very similar patterns. That is, individuals
tried to conserve energy during the daytime in preparation for the
evenings when they ate and drank, often with friends. During
Ramadan also, individuals were more fatigued in the daytime and
napped more often than on control days. This extra fatigue probably
reflected decreased sleep, individuals often having risen earlier
(before sunrise, to prepare for fasting) and retired later (to enable
recovery from the fast).
Some physiological measures and objective measures of
performance (including the response to a bout of exercise) have also
been investigated. Urine osmolality fell during the daytime on
control days as subjects drank, but rose in Ramadan to reach values
at sunset indicative of dehydration. Exercise performance was also
compromised, particularly late in the afternoon when the fast had
lasted several hours. Self-chosen exercise work-rates fell and a set
amount of exercise felt more arduous. There were also changes in
heart rate and lactate accumulation in the blood, indicative of greater
cardiovascular and metabolic stress caused by the exercise in
subjects who had been fasting. Daytime fasting in Ramadan produces
widespread effects which probably reflect combined effects of sleep
loss and restrictions to intakes of water and food.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.
Abstract: In this study spatial-temporal speckle correlation techniques have been applied for the quality evaluation of three different Indian fruits namely apple, pear and tomato for the first time. The method is based on the analysis of variations of laser light scattered from biological samples. The results showed that crosscorrelation coefficients of biospeckle patterns change subject to their freshness and the storage conditions. The biospeckle activity was determined by means of the cross-correlation functions of the intensity fluctuations. Significant changes in biospeckle activity were observed during their shelf lives. From the study, it is found that the biospeckle activity decreases with the shelf-life storage time. Further it has been shown that biospeckle activity changes according to their respiration rates.
Abstract: We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSO-Adaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a three-stage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMU+MIT frontal face dataset.
Abstract: In this paper, a new technique for fast painting with
different colors is presented. The idea of painting relies on applying
masks with different colors to the background. Fast painting is
achieved by applying these masks in the frequency domain instead of
spatial (time) domain. New colors can be generated automatically as a
result from the cross correlation operation. This idea was applied
successfully for faster specific data (face, object, pattern, and code)
detection using neural algorithms. Here, instead of performing cross
correlation between the input input data (e.g., image, or a stream of
sequential data) and the weights of neural networks, the cross
correlation is performed between the colored masks and the
background. Furthermore, this approach is developed to reduce the
computation steps required by the painting operation. The principle of
divide and conquer strategy is applied through background
decomposition. Each background is divided into small in size subbackgrounds
and then each sub-background is processed separately by
using a single faster painting algorithm. Moreover, the fastest painting
is achieved by using parallel processing techniques to paint the
resulting sub-backgrounds using the same number of faster painting
algorithms. In contrast to using only faster painting algorithm, the
speed up ratio is increased with the size of the background when using
faster painting algorithm and background decomposition. Simulation
results show that painting in the frequency domain is faster than that in
the spatial domain.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.
Abstract: In this paper a non-parametric statistical pattern recognition algorithm for the problem of credit scoring will be presented. The proposed algorithm is based on a clustering k- means algorithm and allows for the determination of subclasses of homogenous elements in the data. The algorithm will be tested on two benchmark datasets and its performance compared with other well known pattern recognition algorithm for credit scoring.
Abstract: Knowledge of patterns of genetic diversity enhances
the efficiency of germplasm conservation and improvement. In this
study 96 Iranian landraces of Triticum turgidum originating from
different geographical areas of Iran, along with 18 durum cultivars
from ten countries were evaluated for variation in morphological and
high molecular weight glutenin subunit (HMW-GS) composition.
The first two principal components clearly separated the Iranian
landraces from cultivars. Three alleles were present at the Glu-A1
locus and 11 alleles at Glu-B1. In both cultivars and landraces of
durum wheat, the null allele (Glu-A1c) was observed more
frequently than the Glu-A1a and Glu-A1b alleles. Two alleles,
namely Glu-B1a (subunit 7) and Glu-B1e (subunit 20) represented
the more frequent alleles at Glu-B1 locus. The results showed that
the evaluated Iranian landraces formed an interesting source of
favourable glutenin subunits that might be very desirable in breeding
activities for improving pasta-making quality.