Abstract: Machining parameters are very important in
determining the surface quality of any material. In the past decade,
some new engineering materials were developed for the
manufacturing industry which created a need to conduct an
investigation on the impact of the said parameters on their surface
roughness. Polyurethane (PU) block is widely used in the automotive
industry to manufacture parts such as checking fixtures that are used
to verify the dimensional accuracy of automotive parts. In this paper,
the design of experiment (DOE) was used to investigate on the effect
of the milling parameters on the PU block. Furthermore, an analysis
of the machined surface chemical composition was done using
scanning electron microscope (SEM). It was found that the surface
roughness of the PU block is severely affected when PU undergoes a
flood machining process instead of a dry condition. In addition the
stepover and the silicon content were found to be the most significant
parameters that influence the surface quality of the PU block.
Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: The paper presents a plastic analysis procedure based
on the energy balance concept for performance based seismic retrofit
of multi-story multi-bay masonry infilled reinforced concrete (R/C)
frames with a ‘soft’ ground story using passive energy dissipation
(PED) devices with the objective of achieving a target performance
level of the retrofitted R/C frame for a given seismic hazard level at
the building site. The proposed energy based plastic analysis
procedure was employed for developing performance based design
(PBD) formulations for PED devices for a simulated application in
seismic retrofit of existing frame structures designed in compliance
with the prevalent standard codes of practice. The PBD formulations
developed for PED devices were implemented for simulated seismic
retrofit of a representative code-compliant masonry infilled R/C
frame with a ‘soft’ ground story using friction dampers as the PED
device. Non-linear dynamic analyses of the retrofitted masonry
infilled R/C frames is performed to investigate the efficacy and
accuracy of the proposed energy based plastic analysis procedure in
achieving the target performance level under design level
earthquakes. Results of non-linear dynamic analyses demonstrate that
the maximum inter-story drifts in the masonry infilled R/C frames
with a ‘soft’ ground story that is retrofitted with the friction dampers
designed using the proposed PBD formulations are controlled within
the target drifts under near-field as well far-field earthquakes.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: In this paper, the formulation of a new group explicit
method with a fourth order accuracy is described in solving the two
dimensional Helmholtz equation. The formulation is based on the
nine-point fourth order compact finite difference approximation
formula. The complexity analysis of the developed scheme is also
presented. Several numerical experiments were conducted to test the
feasibility of the developed scheme. Comparisons with other existing
schemes will be reported and discussed. Preliminary results indicate
that this method is a viable alternative high accuracy solver to the
Helmholtz equation.
Abstract: Images are important source of information used as
evidence during any investigation process. Their clarity and accuracy
is essential and of the utmost importance for any investigation.
Images are vulnerable to losing blocks and having noise added to
them either after alteration or when the image was taken initially,
therefore, having a high performance image processing system and it
is implementation is very important in a forensic point of view. This
paper focuses on improving the quality of the forensic images.
For different reasons packets that store data can be affected,
harmed or even lost because of noise. For example, sending the
image through a wireless channel can cause loss of bits. These types
of errors might give difficulties generally for the visual display
quality of the forensic images.
Two of the images problems: noise and losing blocks are covered.
However, information which gets transmitted through any way of
communication may suffer alteration from its original state or even
lose important data due to the channel noise. Therefore, a developed
system is introduced to improve the quality and clarity of the forensic
images.
Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
Abstract: A modeling approach for CMOS gates is presented
based on the use of the equivalent inverter. A new model for the
inverter has been developed using a simplified transistor current
model which incorporates the nanoscale effects for the planar
technology. Parametric expressions for the output voltage are
provided as well as the values of the output and supply current to be
compatible with the CCS technology. The model is parametric
according the input signal slew, output load, transistor widths, supply
voltage, temperature and process. The transistor widths of the
equivalent inverter are determined by HSPICE simulations and
parametric expressions are developed for that using a fitting
procedure. Results for the NAND gate shows that the proposed
approach offers sufficient accuracy with an average error in
propagation delay about 5%.
Abstract: This study is purposed to develop an efficient fault
detection method for Global Navigation Satellite Systems (GNSS)
applications based on adaptive noise covariance estimation. Due to the
dependence on radio frequency signals, GNSS measurements are
dominated by systematic errors in receiver’s operating environment.
In the proposed method, the pseudorange and carrier-phase
measurement noise covariances are obtained at time propagations and
measurement updates in process of Carrier-Smoothed Code (CSC)
filtering, respectively. The test statistics for fault detection are
generated by the estimated measurement noise covariances. To
evaluate the fault detection capability, intentional faults were added to
the filed-collected measurements. The experiment result shows that
the proposed method is efficient in detecting unhealthy measurements
and improves GNSS positioning accuracy against fault occurrences.
Abstract: In this paper, numerical solution of system of
Fredholm and Volterra integral equations by means of the Spline
collocation method is considered. This approximation reduces the
system of integral equations to an explicit system of algebraic
equations. The solution is collocated by cubic B-spline and the
integrand is approximated by the Newton-Cotes formula. The error
analysis of proposed numerical method is studied theoretically. The
results are compared with the results obtained by other methods to
illustrate the accuracy and the implementation of our method.
Abstract: Microarray technology is universally used in the study
of disease diagnosis using gene expression levels. The main
shortcoming of gene expression data is that it includes thousands of
genes and a small number of samples. Abundant methods and
techniques have been proposed for tumor classification using
microarray gene expression data. Feature or gene selection methods
can be used to mine the genes that directly involve in the
classification and to eliminate irrelevant genes. In this paper
statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR)
and F-Statistics are used to rank the genes. The ranked genes are used
for further classification. Particle Swarm Optimization (PSO)
algorithm and Shuffled Frog Leaping (SFL) algorithm are used to
find the significant genes from the top-m ranked genes. The Naïve
Bayes Classifier (NBC) is used to classify the samples based on the
significant genes. The proposed work is applied on Lung and Ovarian
datasets. The experimental results show that the proposed method
achieves 100% accuracy in all the three datasets and the results are
compared with previous works.
Abstract: The parameters of a two-layer soil can be determined by
processing resistivity data obtained from resistivity measurements
carried out on the soil of interest. The processing usually entails
applying the resistivity data as inputs to an optimisation function.
This paper proposes an algorithm which utilises the square error as an
optimisation function. Resistivity data from previous works were
applied to test the accuracy of the new algorithm developed and the
result obtained conforms significantly to results from previous works.
Abstract: A novel simulation method to determine the
displacements of machine tools due to thermal factors is presented.
The specific characteristic of this method is the employment of
original CAD data from the design process chain, which is
interpreted by an algorithm in terms of geometry-based allocation of
convection and radiation parameters. Furthermore analogous models
relating to the thermal behaviour of machine elements are
automatically implemented, which were gained by extensive
experimental testing with thermography imaging. With this a
transient simulation of the thermal field and in series of the
displacement of the machine tool is possible simultaneously during
the design phase. This method was implemented and is already used
industrially in the design of machining centres in order to improve
the quality of herewith manufactured workpieces.
Abstract: Different strategies and tools are available at the oil
and gas industry for detecting and analyzing tension and possible
fractures in borehole walls. Most of these techniques are based on
manual observation of the captured borehole images. While this
strategy may be possible and convenient with small images and few
data, it may become difficult and suitable to errors when big
databases of images must be treated. While the patterns may differ
among the image area, depending on many characteristics (drilling
strategy, rock components, rock strength, etc.). In this work we
propose the inclusion of data-mining classification strategies in order
to create a knowledge database of the segmented curves. These
classifiers allow that, after some time using and manually pointing
parts of borehole images that correspond to tension regions and
breakout areas, the system will indicate and suggest automatically
new candidate regions, with higher accuracy. We suggest the use of
different classifiers methods, in order to achieve different knowledge
dataset configurations.
Abstract: Health analytics (HA) is used in healthcare systems
for effective decision making, management and planning of
healthcare and related activities. However, user resistances, unique
position of medical data content and structure (including
heterogeneous and unstructured data) and impromptu HA projects
have held up the progress in HA applications. Notably, the accuracy
of outcomes depends on the skills and the domain knowledge of the
data analyst working on the healthcare data. Success of HA depends
on having a sound process model, effective project management and
availability of supporting tools. Thus, to overcome these challenges
through an effective process model, we propose a HA process model
with features from rational unified process (RUP) model and agile
methodology.
Abstract: This paper presents a model predictive control (MPC)
of a utility interactive (UI) single phase inverter (SPI) for a
photovoltaic (PV) system at residential/distribution level. The
proposed model uses single-phase phase locked loop (PLL) to
synchronize SPI with the grid and performs MPC control in a dq
reference frame. SPI model consists of boost converter (BC),
maximum power point tracking (MPPT) control, and a full bridge
(FB) voltage source inverter (VSI). No PI regulators to tune and
carrier and modulating waves are required to produce switching
sequence. Instead, the operational model of VSI is used to synthesize
sinusoidal current and track the reference. Model is validated using a
three kW PV system at the input of UI-SPI in Matlab/Simulink.
Implementation and results demonstrate simplicity and accuracy, as
well as reliability of the model.
Abstract: Advances technology in the field of photogrammetry
replaces analog cameras with reflection on aircraft GPS/IMU system
with a digital aerial camera. In this system, when determining the
position of the camera with the GPS, camera rotations are also
determined by the IMU systems. All around the world, digital aerial
cameras have been used for the photogrammetry applications in the
last ten years. In this way, in terms of the work done in
photogrammetry it is possible to use time effectively, costs to be
reduced to a minimum level, the opportunity to make fast and
accurate.
Geo-referencing techniques that are the cornerstone of the GPS /
INS systems, photogrammetric triangulation of images required for
balancing (interior and exterior orientation) brings flexibility to the
process. Also geo-referencing process; needed in the application of
photogrammetry targets to help to reduce the number of ground
control points. In this study, the use of direct and indirect georeferencing
techniques on the accuracy of the points was investigated
in the production of photogrammetric mapping.
Abstract: Nonalcoholic fatty liver disease (NAFLD) has
increased in conjunction with obesity. The accuracy of risk factors
for detecting NAFLD in obese adolescents has not undergone a
formal evaluation. The aim of this study was to evaluate predictors of
NAFLD among Egyptian female obese adolescents. The study
included 162 obese female adolescents. All were subjected to
anthropometry, biochemical analysis and abdominal ultrasongraphic
assessment. Metabolic syndrome (MS) was diagnosed according to
the IDF criteria. Significant association between presence of MS and
NAFLD was observed. Obese adolescents with NAFLD had
significantly higher levels of ALT, triglycerides, fasting glucose,
insulin, blood pressure and HOMA-IR, whereas decreased HDL-C
levels as compared with obese cases without NAFLD. Receiver–
operating characteristic (ROC) curve analysis shows that ALT is a
sensitive predictor for NAFLD, confirming that ALT can be used as a
marker of NAFLD.