Abstract: In this paper, we proposed a method to classify each
type of natural rock texture. Our goal is to classify 26 classes of rock
textures. First, we extract five features of each class by using
principle component analysis combining with the use of applied
spatial frequency measurement. Next, the effective node number of
neural network was tested. We used the most effective neural
network in classification process. The results from this system yield
quite high in recognition rate. It is shown that high recognition rate
can be achieved in separation of 26 stone classes.
Abstract: As the majority of faults are found in a few of its
modules so there is a need to investigate the modules that are
affected severely as compared to other modules and proper
maintenance need to be done in time especially for the critical
applications. As, Neural networks, which have been already applied
in software engineering applications to build reliability growth
models predict the gross change or reusability metrics. Neural
networks are non-linear sophisticated modeling techniques that are
able to model complex functions. Neural network techniques are
used when exact nature of input and outputs is not known. A key
feature is that they learn the relationship between input and output
through training. In this present work, various Neural Network Based
techniques are explored and comparative analysis is performed for
the prediction of level of need of maintenance by predicting level
severity of faults present in NASA-s public domain defect dataset.
The comparison of different algorithms is made on the basis of Mean
Absolute Error, Root Mean Square Error and Accuracy Values. It is
concluded that Generalized Regression Networks is the best
algorithm for classification of the software components into different
level of severity of impact of the faults. The algorithm can be used to
develop model that can be used for identifying modules that are
heavily affected by the faults.
Abstract: The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.
Abstract: This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
Abstract: Gene, principal unit of inheritance, is an ordered
sequence of nucleotides. The genes of eukaryotic organisms include
alternating segments of exons and introns. The region of
Deoxyribonucleic acid (DNA) within a gene containing instructions
for coding a protein is called exon. On the other hand, non-coding
regions called introns are another part of DNA that regulates gene
expression by removing from the messenger Ribonucleic acid (RNA)
in a splicing process. This paper proposes to determine splice
junctions that are exon-intron boundaries by analyzing DNA
sequences. A splice junction can be either exon-intron (EI) or intron
exon (IE). Because of the popularity and compatibility of the
artificial neural network (ANN) in genetic fields; various ANN
models are applied in this research. Multi-layer Perceptron (MLP),
Radial Basis Function (RBF) and Generalized Regression Neural
Networks (GRNN) are used to analyze and detect the splice junctions
of gene sequences. 10-fold cross validation is used to demonstrate
the accuracy of networks. The real performances of these networks
are found by applying Receiver Operating Characteristic (ROC)
analysis.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: An adaptive neural network controller for
autonomous underwater vehicles (AUVs) is presented in this paper.
The AUV model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
In this regards, a nonlinear neural network is used to approximate the
nonlinear uncertainties of AUV dynamics, thus overcoming some
limitations of conventional controllers and ensure good performance.
The uniform ultimate boundedness of AUV tracking errors and the
stability of the proposed control system are guaranteed based on
Lyapunov theory. Numerical simulation studies for motion control of
an AUV are performed to demonstrate the effectiveness of the
proposed controller.
Abstract: In this paper, we construct and implement a new
Steganography algorithm based on learning system to hide a large
amount of information into color BMP image. We have used adaptive
image filtering and adaptive non-uniform image segmentation with
bits replacement on the appropriate pixels. These pixels are selected
randomly rather than sequentially by using new concept defined by
main cases with sub cases for each byte in one pixel. According to
the steps of design, we have been concluded 16 main cases with their
sub cases that covere all aspects of the input information into color
bitmap image. High security layers have been proposed through four
layers of security to make it difficult to break the encryption of the
input information and confuse steganalysis too. Learning system has
been introduces at the fourth layer of security through neural
network. This layer is used to increase the difficulties of the statistical
attacks. Our results against statistical and visual attacks are discussed
before and after using the learning system and we make comparison
with the previous Steganography algorithm. We show that our
algorithm can embed efficiently a large amount of information that
has been reached to 75% of the image size (replace 18 bits for each
pixel as a maximum) with high quality of the output.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: In this paper, we propose to study the synthesis of the
vertical dipole antenna over imperfect ground. The synthesis
implementation-s method for this type of antenna permits to
approach the appropriated radiance-s diagram. The used approach is
based on neural network. Our main contribution in this paper is the
extension of a synthesis model of this vertical dipole antenna over
imperfect ground.
Abstract: The new idea of analyze of power system failure with
use of artificial neural network is proposed. An analysis of the
possibility of simulating phenomena accompanying system faults and
restitution is described. It was indicated that the universal model for
the simulation of phenomena in whole analyzed range does not exist.
The main classic method of search of optimal structure and
parameter identification are described shortly. The example with
results of calculation is shown.
Abstract: In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.
Abstract: The paper presents the potential of fuzzy logic (FL-I)
and neural network techniques (ANN-I) for predicting the
compressive strength, for SCC mixtures. Six input parameters that is
contents of cement, sand, coarse aggregate, fly ash, superplasticizer
percentage and water-to-binder ratio and an output parameter i.e. 28-
day compressive strength for ANN-I and FL-I are used for modeling.
The fuzzy logic model showed better performance than neural
network model.
Abstract: Recently, fast neural networks for object/face
detection were presented in [1-3]. The speed up factor of these
networks relies on performing cross correlation in the frequency
domain between the input image and the weights of the hidden
layer. But, these equations given in [1-3] for conventional and fast
neural networks are not valid for many reasons presented here. In
this paper, correct equations for cross correlation in the spatial and
frequency domains are presented. Furthermore, correct formulas for
the number of computation steps required by conventional and fast
neural networks given in [1-3] are introduced. A new formula for
the speed up ratio is established. Also, corrections for the equations
of fast multi scale object/face detection are given. Moreover,
commutative cross correlation is achieved. Simulation results show
that sub-image detection based on cross correlation in the frequency
domain is faster than classical neural networks.
Abstract: Here, a new idea to speed up the operation of
complex valued time delay neural networks is presented. The whole
data are collected together in a long vector and then tested as a one
input pattern. The proposed fast complex valued time delay neural
networks uses cross correlation in the frequency domain between the
tested data and the input weights of neural networks. It is proved
mathematically that the number of computation steps required for
the presented fast complex valued time delay neural networks is less
than that needed by classical time delay neural networks. Simulation
results using MATLAB confirm the theoretical computations.
Abstract: In this paper, in order to categorize ORL database face
pictures, principle Component Analysis (PCA) and Kernel Principal
Component Analysis (KPCA) methods by using Elman neural
network and Support Vector Machine (SVM) categorization methods
are used. Elman network as a recurrent neural network is proposed
for modeling storage systems and also it is used for reviewing the
effect of using PCA numbers on system categorization precision rate
and database pictures categorization time. Categorization stages are
conducted with various components numbers and the obtained results
of both Elman neural network categorization and support vector
machine are compared. In optimum manner 97.41% recognition
accuracy is obtained.
Abstract: The purpose of this study is mainly to predict collision
frequency on the horizontal tangents combined with vertical curves
using artificial neural network methods. The proposed ANN models
are compared with existing regression models. First, the variables
that affect collision frequency were investigated. It was found that
only the annual average daily traffic, section length, access density,
the rate of vertical curvature, smaller curve radius before and after
the tangent were statistically significant according to related
combinations. Second, three statistical models (negative binomial,
zero inflated Poisson and zero inflated negative binomial) were
developed using the significant variables for three alignment
combinations. Third, ANN models are developed by applying the
same variables for each combination. The results clearly show that
the ANN models have the lowest mean square error value than those
of the statistical models. Similarly, the AIC values of the ANN
models are smaller to those of the regression models for all the
combinations. Consequently, the ANN models have better statistical
performances than statistical models for estimating collision
frequency. The ANN models presented in this paper are
recommended for evaluating the safety impacts 3D alignment
elements on horizontal tangents.