Abstract: This paper aims to develop a NOx emission model of
an acid gas incinerator using Nelder-Mead least squares support
vector regression (LS-SVR). Malaysia DOE is actively imposing the
Clean Air Regulation to mandate the installation of analytical
instrumentation known as Continuous Emission Monitoring System
(CEMS) to report emission level online to DOE . As a hardware
based analyzer, CEMS is expensive, maintenance intensive and often
unreliable. Therefore, software predictive technique is often
preferred and considered as a feasible alternative to replace the
CEMS for regulatory compliance. The LS-SVR model is built based
on the emissions from an acid gas incinerator that operates in a LNG
Complex. Simulated Annealing (SA) is first used to determine the
initial hyperparameters which are then further optimized based on the
performance of the model using Nelder-Mead simplex algorithm.
The LS-SVR model is shown to outperform a benchmark model
based on backpropagation neural networks (BPNN) in both training
and testing data.
Abstract: We board the problem of creating a seismic alert
system, based upon artificial neural networks, trained by using the
well-known back-propagation and genetic algorithms, in order to emit
the alarm for the population located into a specific city, about an
eminent earthquake greater than 4.5 Richter degrees, and avoiding
disasters and human loses. In lieu of using the propagation wave, we
employed the magnitude of the earthquake, to establish a correlation
between the recorded magnitudes from a controlled area and the city,
where we want to emit the alarm. To measure the accuracy of the
posed method, we use a database provided by CIRES, which contains
the records of 2500 quakes incoming from the State of Guerrero
and Mexico City. Particularly, we performed the proposed method to
generate an issue warning in Mexico City, employing the magnitudes
recorded in the State of Guerrero.
Abstract: Every commercial bank optimises its asset portfolio
depending on the profitability of assets and chosen or imposed
constraints. This paper proposes and applies a stylized model for
optimising banks' asset and liability structure, reflecting profitability
of different asset categories and their risks as well as costs associated
with different liability categories and reserve requirements. The level
of detail for asset and liability categories is chosen to create a
suitably parsimonious model and to include the most important
categories in the model. It is shown that the most appropriate
optimisation criterion for the model is the maximisation of the ratio
of net interest income to assets. The maximisation of this ratio is
subject to several constraints. Some are accounting identities or
dictated by legislative requirements; others vary depending on the
market objectives for a particular bank. The model predicts variable
amount of assets allocated to loan provision.
Abstract: In this paper, an improved technique for contingency
ranking using artificial neural network (ANN) is presented. The
proposed approach is based on multi-layer perceptrons trained by
backpropagation to contingency analysis. Severity indices in dynamic
stability assessment are presented. These indices are based on the
concept of coherency and three dot products of the system variables.
It is well known that some indices work better than others for a
particular power system. This paper along with test results using
several different systems, demonstrates that combination of indices
with ANN provides better ranking than a single index. The presented
results are obtained through the use of power system simulation
(PSS/E) and MATLAB 6.5 software.
Abstract: The zinc and iron environments in different growth
stages have been studied with EXAFS and XANES with Brookhaven
Synchrotron Light Source. Tissue samples included meat, organ,
vegetable, leaf, and yeast. The project studied the EXAFS and
XANES of tissue samples using Zn and Fe K-edges. Duck embryo
samples show that brain and intestine would contain shorter EXFAS
determined Zn-N/O bond; as with the cases of fresh yeast versus
reconstituted live yeast and green leaf versus yellow leaf. The
XANES Fourier transform characteristic-length would be useful as a
functionality index for selected types of tissue samples in various
physical states. The extension to the development of functional
synchrotron imaging for tissue engineering application based on
spectroscopic technique is discussed.
Abstract: The Beshar River is one aquatic ecosystem, which is
located next to the city of Yasuj in southern Iran. The Beshar river
has been contaminated by industrial factories such as effluent of
sugar factory, agricultural and other activities in this region such as,
Imam Sajjad hospital, drainage from agricultural farms, Yasuj urban
surface runoff and effluent of wastewater treatment plants ,specially
Yasuj waste water treatment plant. In order to evaluate the effects of
these pollutants on the quality of the Beshar river, five monitoring
stations were selected along its course. The first station is located
upstream of Yasuj near the Dehnow village; stations 2 to 4 are
located east, south and west of city; and the 5th station is located
downstream of Yasuj. Several water quality parameters were
sampled. These include pH, dissolved oxygen, biological oxygen
demand (BOD), temperature, conductivity, turbidity, total dissolved
solids and discharge or flow measurements. Water samples from the
five stations were collected and analyzed to determine the following
physicochemical parameters: EC, pH, T.D.S, T.H, No2, DO, BOD5,
COD during 2008 to 2010. The study shows that the BOD5 value of
station 1 is at a minimum (1.7 ppm) and increases downstream from
stations 2 to 4 to a maximum (11.6 ppm), and then decreases at
station 5. The DO values of station 1 is a maximum (8.45 ppm),
decreases downstream to stations 2 - 4 which are at a minimum (3.1
ppm), before increasing at station 5. The amount of BOD and TDS
are highest at the 4th station and the amount of DO is lowest at this
station, marking the 4th station as more highly polluted than the
other stations .This study shows average amount of the water quality
parameters in first year of sampling (2008) have had a better quality
relation to third year in 2010 because of recent drought in this region
and pollutant increasing .As the Beshar river path after 5th station
goes through the mountain area with more slope and flow velocity
,so the physicochemical parameters improve at the 5th station due to
pollutant degradation and dilution. Finally the point and nonpoint
pollutant sources of Beshar river were determined and compared to
the monitoring results.
Abstract: We evaluated the effect of sensory (direct current
(DC), 600μA) and motor (monophasic current, pulse duration 300μs,
100 Hz, 2.5-3mA) intensities of cathodal electrical stimulation (ES)
current to release VEGF and biomechanical properties of wound. 54
male Sprague-dawley rats were randomly assigned into one control
and two experimental groups. A full thickness skin incision was
made on animals- dorsal region. The experimental groups received
ES for 1h/day and every other day. VEGF expression was measured
in skin on the 7th day after surgical incision and tensile strength was
measured on 21st day. On the 7th day, the values of skin VEGF in the
sensory group were significantly greater than those of the other
groups (p < 0.05). Sensory and Motor intensity stimulation, can not
improve the biomechanical properties of the repaired wounds.
It seems the mechanical environment induced by sensory and
motor intensity of electrical stimulation, could not simulate the role
of normal daily stress and strain to maturation of collagen fibers and
their cross links. Further work is needed to determine the relationship
between VEGF expression after ES and its effect on tensile strength
of healed wound.
Abstract: In a world of climate change and limited fossil fuel resources, renewable energy sources are playing an increasingly important role. Due to industrializations and population growth our economy and technologies today largely depend upon natural resources, which are not replaceable. Approximately 90% of our energy consumption comes from fossil fuels (viz. coal, oil and natural gas). The irony is that these resources are depleting. Also, the huge consumption of fossil fuels has caused visible damage to the environment in various forms viz. global warming, acid rains etc.
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: 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: 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.
Abstract: The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.
Abstract: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
Abstract: Banishing hunger from the face of earth has been
frequently expressed in various international, national and regional
level conferences since 1974. Providing food security has become
important issue across the world particularly in developing countries.
In a developing country like India, where growth rate of population is
more than that of the food grains production, food security is a
question of great concern. According to the International Food Policy
Research Institute's Global Hunger Index, 2011, India ranks 67 of the
81 countries of the world with the worst food security status. After
Green Revolution, India became a food surplus country. Its
production has increased from 74.23 million tonnes in 1966-67 to
257.44 million tonnes in 2011-12. But after achieving selfsufficiency
in food during last three decades, the country is now
facing new challenges due to increasing population, climate change,
stagnation in farm productivity. Therefore, the main objective of the
present paper is to examine the food security situation at national
level in the country and further to explain the paradox of food
insecurity in a food surplus state of India i.e in Punjab at micro level.
In order to achieve the said objectives, secondary data collected from
the Ministry of Agriculture and the Agriculture department of Punjab
State was analyzed. The result of the study showed that despite
having surplus food production the country is still facing food
insecurity problem at micro level. Within the Kandi belt of Punjab
state, the area adjacent to plains is food secure while the area along
the hills falls in food insecure zone.
The present paper is divided into following three sections (i)
Introduction, (ii) Analysis of food security situation at national level
as well as micro level (Kandi belt of Punjab State) (iii) Concluding
Observations
Abstract: In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.
Abstract: In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.
Abstract: It is well known that a linear dynamic system including
a delay will exhibit limit cycle oscillations when a bang-bang sensor
is used in the feedback loop of a PID controller. A similar behaviour
occurs when a delayed feedback signal is used to train a neural
network. This paper develops a method of predicting this behaviour
by linearizing the system, which can be shown to behave in a manner
similar to an integral controller. Using this procedure, it is possible
to predict the characteristics of the neural network driven limit cycle
to varying degrees of accuracy, depending on the information known
about the system. An application is also presented: the intelligent
control of a spark ignition engine.
Abstract: A dent is a gross distortion of the pipe cross-section.
Dent depth is defined as the maximum reduction in the diameter of
the pipe compared to the original diameter. Pipeline dent finite
element (FE) simulation and theoretical analysis are conducted in this
paper to develop an understanding of the geometric characteristics
and strain distribution in the pressurized dented pipe. Based on the
results, the magnitude of the denting force increases significantly
with increasing the internal pressure, and the maximum
circumferential and longitudinal strains increase by increasing the
internal pressure and the dent depth. The results can be used for
characterizing dents and ranking their risks to the integrity of a
pipeline.
Abstract: Prediction of viscosity of natural gas is an important parameter in the energy industries such as natural gas storage and transportation. In this study viscosity of different compositions of natural gas is modeled by using an artificial neural network (ANN) based on back-propagation method. A reliable database including more than 3841 experimental data of viscosity for testing and training of ANN is used. The designed neural network can predict the natural gas viscosity using pseudo-reduced pressure and pseudo-reduced temperature with AARD% of 0.221. The accuracy of designed ANN has been compared to other published empirical models. The comparison indicates that the proposed method can provide accurate results.
Abstract: In this paper delamination phenomenon in
Carbon-Epoxy laminated composite material is investigated
numerically. Arcan apparatus and specimen is modeled in ABAQUS
finite element software for different loading conditions and crack
geometries. The influence of variation of crack geometry on
interlaminar fracture stress intensity factor and energy release rate for
various mixed mode ratios and pure mode I and II was studied. Also,
correction factors for this specimen for different crack length ratios
were calculated. The finite element results indicate that for loading
angles close to pure mode-II loading, a high ratio of mode-II to
mode-I fracture is dominant and there is an opposite trend for loading
angles close to pure mode-I loading. It confirms that by varying the
loading angle of Arcan specimen pure mode-I, pure mode-II and a
wide range of mixed-mode loading conditions can be created and
tested. Also, numerical results confirm that the increase of the mode-
II loading contribution leads to an increase of fracture resistance in
the CF/PEI composite (i.e., a reduction in the total strain energy
release rate) and the increase of the crack length leads to a reduction
of interlaminar fracture resistance in the CF/PEI composite (i.e., an
increase in the total interlaminar strain energy release rate).