Abstract: The objective of this paper is to study the electrical
resistivity complexity between field and laboratory measurement, in
order to improve the effectiveness of data interpretation for
geophysical ground resistivity survey. The geological outcrop in
Penang, Malaysia with an obvious layering contact was chosen as the
study site. Two dimensional geoelectrical resistivity imaging were
used in this study to maps the resistivity distribution of subsurface,
whereas few subsurface sample were obtained for laboratory
advance. In this study, resistivity of samples in original conditions is
measured in laboratory by using time domain low-voltage technique,
particularly for granite core sample and soil resistivity measuring set
for soil sample. The experimentation results from both schemes are
studied, analyzed, calibrated and verified, including basis and
correlation, degree of tolerance and characteristics of substance.
Consequently, the significant different between both schemes is
explained comprehensively within this paper.
Abstract: The aluminum bronze matrix alumina composites using hot press and resin infiltration were investigated to study their porosities, hardness, bending strengths, and microstructures. The experiment results show that the hardness of the sintered composites with the decrease of porosity increases. The composites without and with resin infiltration have about HRF 42-61 of about 34-40% of porosity and about HRF 62-83 of about 30-36% of porosity, respectively. Besides, the alumina composites contain a more amount of iron and nickel powders would cause a lower bending strength due to forming some weaker bonding among the iron, nickel, copper, aluminum under this hot pressing of shorter time.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: This paper proposes a method, combining color and
layout features, for identifying documents captured from lowresolution
handheld devices. On one hand, the document image color
density surface is estimated and represented with an equivalent
ellipse and on the other hand, the document shallow layout structure
is computed and hierarchically represented. The combined color and
layout features are arranged in a symbolic file, which is unique for
each document and is called the document-s visual signature. Our
identification method first uses the color information in the
signatures in order to focus the search space on documents having a
similar color distribution, and finally selects the document having the
most similar layout structure in the remaining search space. Finally,
our experiment considers slide documents, which are often captured
using handheld devices.
Abstract: A conventional binding method for low power in a
high-level synthesis mainly focuses on finding an optimal binding for
an assumed input data, and obtains only one binding table. In this
paper, we show that a binding method which uses multiple binding
tables gets better solution compared with the conventional methods
which use a single binding table, and propose a dynamic bus binding
scheme for low power using multiple binding tables. The proposed
method finds multiple binding tables for the proper partitions of an
input data, and switches binding tables dynamically to produce the
minimum total switching activity. Experimental result shows that the
proposed method obtains a binding solution having 12.6-28.9%
smaller total switching activity compared with the conventional
methods.
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: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: Different numerical methods are employed and developed for simulating interfacial flows. A large range of applications belong to this group, e.g. two-phase flows of air bubbles in water or water drops in air. In such problems surface tension effects often play a dominant role. In this paper, various models of surface tension force for interfacial flows, the CSF, CSS, PCIL and SGIP models have been applied to simulate the motion of small air bubbles in water and the results were compared and reviewed. It has been pointed out that by using SGIP or PCIL models, we are able to simulate bubble rise and obtain results in close agreement with the experimental data.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: The aim of the present study is to analyze the generation of osteoporotic vertebral bone induced by lack of calcium during growth period and analyze its effects for disc degeneration, based on biomechanical and histomorphometrical study. Mechanical and histomorphological characteristics of lumbar vertebral bones and discs of rats with calcium free diet (CFD) were detected and tracked by using high resolution in-vivo micro-computed tomography (in-vivo micro-CT), finite element (FE) and histological analysis. Twenty female Sprague-Dawley rats (6 weeks old, approximate weight 170g) were randomly divided into two groups (CFD group: 10, NOR group: 10). The CFD group was maintained on a refmed calcium-controlled semisynthetic diet without added calcium, to induce osteoporosis. All lumbar (L 1-L6) were scanned by using in vivo micro-CT with 35i.un resolution at 0, 4, 8 weeks to track the effects of CFD on the generation of osteoporosis. The fmdings of the present study indicated that calcium insufficiency was the main factor in the generation of osteoporosis and it induced lumbar vertebral disc degeneration. This study is a valuable experiment to firstly evaluate osteoporotic vertebral bone and disc degeneration induced by lack of calcium during growth period from a biomechanical and histomorphometrical point of view.
Abstract: In order to assess optical fiber reliability in different environmental and stress conditions series of testing are performed simulating overlapping of chemical and mechanical controlled varying factors. Each series of testing may be compared using statistical processing: i.e. Weibull plots. Due to the numerous data to treat, a software application has appeared useful to interpret selected series of experiments in function of envisaged factors. The current paper presents a software application used in the storage, modelling and interpretation of experimental data gathered from optical fibre testing. The present paper strictly deals with the software part of the project (regarding the modelling, storage and processing of user supplied data).
Abstract: In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.
Abstract: Removal of a reactive dye (Reactive blue 4) by
adsorption utilizing waste aluminium hydroxide sludge as an
adsorbent was investigated. The removal of the dye was optimized
using response surface methodology (RSM). In the RSM
experiments; initial dye concentration, adsorbent concentration and
contact time were critical parameters. RSM experiments were
performed at the range of initial dye concentration 31.82-368.18
mg/L, adsorbent concentration 3.18-36.82 g/L, contact time 15.82-
56.18 h. Optimum initial dye concentration, adsorbent concentration
and contact time were obtained as 108.83 mg/L, 29.36 g/L and 33.57
h respectively. At these conditions, maximum removal of the dye was
obtained as 95%. The experiments were performed at the optimum
conditions to verify these results and the same results were obtained.
Abstract: Digital watermarking has become an important technique for copyright protection but its robustness against attacks remains a major problem. In this paper, we propose a normalizationbased robust image watermarking scheme. In the proposed scheme, original host image is first normalized to a standard form. Zernike transform is then applied to the normalized image to calculate Zernike moments. Dither modulation is adopted to quantize the magnitudes of Zernike moments according to the watermark bit stream. The watermark extracting method is a blind method. Security analysis and false alarm analysis are then performed. The quality degradation of watermarked image caused by the embedded watermark is visually transparent. Experimental results show that the proposed scheme has very high robustness against various image processing operations and geometric attacks.
Abstract: This research proposes an algorithm for the simulation
of time-periodic unsteady problems via the solution unsteady Euler
and Navier-Stokes equations. This algorithm which is called Time
Spectral method uses a Fourier representation in time and hence
solve for the periodic state directly without resolving transients
(which consume most of the resources in a time-accurate scheme).
Mathematical tools used here are discrete Fourier transformations. It
has shown tremendous potential for reducing the computational cost
compared to conventional time-accurate methods, by enforcing
periodicity and using Fourier representation in time, leading to
spectral accuracy. The accuracy and efficiency of this technique is
verified by Euler and Navier-Stokes calculations for pitching airfoils.
Because of flow turbulence nature, Baldwin-Lomax turbulence
model has been used at viscous flow analysis. The results presented
by the Time Spectral method are compared with experimental data. It
has shown tremendous potential for reducing the computational cost
compared to the conventional time-accurate methods, by enforcing
periodicity and using Fourier representation in time, leading to
spectral accuracy, because results verify the small number of time
intervals per pitching cycle required to capture the flow physics.
Abstract: In this paper we address the problem of musical style
classification, which has a number of applications like indexing in
musical databases or automatic composition systems. Starting from
MIDI files of real-world improvisations, we extract the melody track
and cut it into overlapping segments of equal length. From these
fragments, some numerical features are extracted as descriptors of
style samples. We show that a standard Bayesian classifier can be
conveniently employed to build an effective musical style classifier,
once this set of features has been extracted from musical data.
Preliminary experimental results show the effectiveness of the
developed classifier that represents the first component of a musical
audio retrieval system
Abstract: The objective of this study is to design an adaptive
neuro-fuzzy inference system (ANFIS) for estimation of surface
roughness in grinding process. The Used data have been generated
from experimental observations when the wheel has been dressed
using a rotary diamond disc dresser. The input parameters of model
are dressing speed ratio, dressing depth and dresser cross-feed rate
and output parameter is surface roughness. In the experimental
procedure the grinding conditions are constant and only the dressing
conditions are varied. The comparison of the predicted values and the
experimental data indicates that the ANFIS model has a better
performance with respect to back-propagation neural network
(BPNN) model which has been presented by the authors in previous
work for estimation of the surface roughness.
Abstract: This paper present the implementation of a new ordering strategy on Successive Overrelaxation scheme on two dimensional boundary value problems. The strategy involve two directions alternatingly; from top and bottom of the solution domain. The method shows to significantly reduce the iteration number to converge. Four numerical experiments were carried out to examine the performance of the new strategy.
Abstract: Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today's commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98% detection rate (DR) in comparison with
other existing methods.