Abstract: LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: One major source of performance decline in speaker
recognition system is channel mismatch between training and testing.
This paper focuses on improving channel robustness of speaker
recognition system in two aspects of channel compensation technique
and channel robust features. The system is text-independent speaker
identification system based on two-stage recognition. In the aspect of
channel compensation technique, this paper applies MAP (Maximum
A Posterior Probability) channel compensation technique, which was
used in speech recognition, to speaker recognition system. In the
aspect of channel robust features, this paper introduces
pitch-dependent features and pitch-dependent speaker model for the
second stage recognition. Based on the first stage recognition to
testing speech using GMM (Gaussian Mixture Model), the system
uses GMM scores to decide if it needs to be recognized again. If it
needs to, the system selects a few speakers from all of the speakers
who participate in the first stage recognition for the second stage
recognition. For each selected speaker, the system obtains 3
pitch-dependent results from his pitch-dependent speaker model, and
then uses ANN (Artificial Neural Network) to unite the 3
pitch-dependent results and 1 GMM score for getting a fused result.
The system makes the second stage recognition based on these fused
results. The experiments show that the correct rate of two-stage
recognition system based on MAP channel compensation technique
and pitch-dependent features is 41.7% better than the baseline system
for closed-set test.
Abstract: Artificial Neural Network (ANN)s can be modeled for
High Energy Particle analysis with special emphasis on shower core
location. The work describes the use of an ANN based system which
has been configured to predict locations of cores of showers in the
range 1010.5 to 1020.5 eV. The system receives density values as
inputs and generates coordinates of shower events recorded for values
captured by 20 core positions and 80 detectors in an area of 100
meters. Twenty ANNs are trained for the purpose and the positions
of shower events optimized by using cooperative ANN learning. The
results derived with variations of input upto 50% show success rates
in the range of 90s.
Abstract: This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network
management.
Abstract: An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Abstract: Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
Abstract: Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Abstract: The aim of the article is extending and developing
econometrics and network structure based methods which are able to
distinguish price manipulation in Tehran stock exchange. The
principal goal of the present study is to offer model for
approximating price manipulation in Tehran stock exchange. In order
to do so by applying separation method a sample consisting of 397
companies accepted at Tehran stock exchange were selected and
information related to their price and volume of trades during years
2001 until 2009 were collected and then through performing runs
test, skewness test and duration correlative test the selected
companies were divided into 2 sets of manipulated and non
manipulated companies. In the next stage by investigating
cumulative return process and volume of trades in manipulated
companies, the date of starting price manipulation was specified and
in this way the logit model, artificial neural network, multiple
discriminant analysis and by using information related to size of
company, clarity of information, ratio of P/E and liquidity of stock
one year prior price manipulation; a model for forecasting price
manipulation of stocks of companies present in Tehran stock
exchange were designed. At the end the power of forecasting models
were studied by using data of test set. Whereas the power of
forecasting logit model for test set was 92.1%, for artificial neural
network was 94.1% and multi audit analysis model was 90.2%;
therefore all of the 3 aforesaid models has high power to forecast
price manipulation and there is no considerable difference among
forecasting power of these 3 models.
Abstract: The paper presents a comparative performance of the
models developed to predict 28 days compressive strengths using
neural network techniques for data taken from literature (ANN-I) and
data developed experimentally for SCC containing bottom ash as
partial replacement of fine aggregates (ANN-II). The data used in the
models are arranged in the format of six and eight input parameters
that cover the contents of cement, sand, coarse aggregate, fly ash as
partial replacement of cement, bottom ash as partial replacement of
sand, water and water/powder ratio, superplasticizer dosage and an
output parameter that is 28-days compressive strength and
compressive strengths at 7 days, 28 days, 90 days and 365 days,
respectively for ANN-I and ANN-II. The importance of different
input parameters is also given for predicting the strengths at various
ages using neural network. The model developed from literature data
could be easily extended to the experimental data, with bottom ash as
partial replacement of sand with some modifications.
Abstract: Computer worm detection is commonly performed by
antivirus software tools that rely on prior explicit knowledge of the
worm-s code (detection based on code signatures). We present an
approach for detection of the presence of computer worms based on
Artificial Neural Networks (ANN) using the computer's behavioral
measures. Identification of significant features, which describe the
activity of a worm within a host, is commonly acquired from security
experts. We suggest acquiring these features by applying feature
selection methods. We compare three different feature selection
techniques for the dimensionality reduction and identification of the
most prominent features to capture efficiently the computer behavior
in the context of worm activity. Additionally, we explore three
different temporal representation techniques for the most prominent
features. In order to evaluate the different techniques, several
computers were infected with five different worms and 323 different
features of the infected computers were measured. We evaluated
each technique by preprocessing the dataset according to each one
and training the ANN model with the preprocessed data. We then
evaluated the ability of the model to detect the presence of a new
computer worm, in particular, during heavy user activity on the
infected computers.
Abstract: Analysis of blood vessel mechanics in normal and
diseased conditions is essential for disease research, medical device
design and treatment planning. In this work, 3D finite element
models of normal vessel and atherosclerotic vessel with 50% plaque
deposition were developed. The developed models were meshed
using finite number of tetrahedral elements. The developed models
were simulated using actual blood pressure signals. Based on the
transient analysis performed on the developed models, the parameters
such as total displacement, strain energy density and entropy per unit
volume were obtained. Further, the obtained parameters were used to
develop artificial neural network models for analyzing normal and
atherosclerotic blood vessels. In this paper, the objectives of the
study, methodology and significant observations are presented.
Abstract: In recent years with the rapid development of Internet and the Web, more and more web applications have been deployed in many fields and organizations such as finance, military, and government. Together with that, hackers have found more subtle ways to attack web applications. According to international statistics, SQL Injection is one of the most popular vulnerabilities of web applications. The consequences of this type of attacks are quite dangerous, such as sensitive information could be stolen or authentication systems might be by-passed. To mitigate the situation, several techniques have been adopted. In this research, a security solution is proposed using Artificial Neural Network to protect web applications against this type of attacks. The solution has been experimented on sample datasets and has given promising result. The solution has also been developed in a prototypic web application firewall called ANNbWAF.
Abstract: This paper presents Simulation and experimental
study aimed at investigating the effectiveness of an adaptive artificial
neural network stabilizer on enhancing the damping torque of a
synchronous generator. For this purpose, a power system comprising
a synchronous generator feeding a large power system through a
short tie line is considered. The proposed adaptive neuro-control
system consists of two multi-layered feed forward neural networks,
which work as a plant model identifier and a controller. It generates
supplementary control signals to be utilized by conventional
controllers. The details of the interfacing circuits, sensors and
transducers, which have been designed and built for use in tests, are
presented. The synchronous generator is tested to investigate the
effect of tuning a Power System Stabilizer (PSS) on its dynamic
stability. The obtained simulation and experimental results verify the
basic theoretical concepts.
Abstract: Evolutionary Programming (EP) represents a
methodology of Evolutionary Algorithms (EA) in which mutation is
considered as a main reproduction operator. This paper presents a
novel EP approach for Artificial Neural Networks (ANN) learning.
The proposed strategy consists of two components: the self-adaptive,
which contains phenotype information and the dynamic, which is
described by genotype. Self-adaptation is achieved by the addition of
a value, called the network weight, which depends on a total number
of hidden layers and an average number of neurons in hidden layers.
The dynamic component changes its value depending on the fitness
of a chromosome, exposed to mutation. Thus, the mutation step size
is controlled by two components, encapsulated in the algorithm,
which adjust it according to the characteristics of a predefined ANN
architecture and the fitness of a particular chromosome. The
comparative analysis of the proposed approach and the classical EP
(Gaussian mutation) showed, that that the significant acceleration of
the evolution process is achieved by using both phenotype and
genotype information in the mutation strategy.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: In Supply Chain Management (SCM), strengthening partnerships with suppliers is a significant factor for enhancing competitiveness. Hence, firms increasingly emphasize supplier evaluation processes. Supplier evaluation systems are basically developed in terms of criteria such as quality, cost, delivery, and flexibility. Because there are many variables to be analyzed, this process becomes hard to execute and needs expertise. On this account, this study aims to develop an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). The methods are applied on the data of 24 suppliers, which have longterm relationships with a medium sized company from German Iron and Steel Industry. The data of suppliers consists of variables such as material quality (MQ), discount of amount (DOA), discount of cash (DOC), payment term (PT), delivery time (DT) and annual revenue (AR). Meanwhile, the efficiency that is generated by using DEA is added to the supplier evaluation system in order to use them as system outputs.
Abstract: Heart failure is the most common reason of death
nowadays, but if the medical help is given directly, the patient-s life
may be saved in many cases. Numerous heart diseases can be
detected by means of analyzing electrocardiograms (ECG). Artificial
Neural Networks (ANN) are computer-based expert systems that
have proved to be useful in pattern recognition tasks. ANN can be
used in different phases of the decision-making process, from
classification to diagnostic procedures. This work concentrates on a
review followed by a novel method.
The purpose of the review is to assess the evidence of healthcare
benefits involving the application of artificial neural networks to the
clinical functions of diagnosis, prognosis and survival analysis, in
ECG signals. The developed method is based on a compound neural
network (CNN), to classify ECGs as normal or carrying an
AtrioVentricular heart Block (AVB). This method uses three
different feed forward multilayer neural networks. A single output
unit encodes the probability of AVB occurrences. A value between 0
and 0.1 is the desired output for a normal ECG; a value between 0.1
and 1 would infer an occurrence of an AVB. The results show that
this compound network has a good performance in detecting AVBs,
with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy
value is 87.9%.
Abstract: The back propagation algorithm calculates the weight
changes of artificial neural networks, and a common approach is to
use a training algorithm consisting of a learning rate and a
momentum factor. The major drawbacks of above learning algorithm
are the problems of local minima and slow convergence speeds. The
addition of an extra term, called a proportional factor reduces the
convergence of the back propagation algorithm. We have applied the
three term back propagation to multiplicative neural network
learning. The algorithm is tested on XOR and parity problem and
compared with the standard back propagation training algorithm.