Abstract: Nowadays, there is little information, concerning the
heat shield systems, and this information is not completely reliable to
use in so many cases. for example, the precise calculation cannot be
done for various materials. In addition, the real scale test has two
disadvantages: high cost and low flexibility, and for each case we
must perform a new test. Hence, using numerical modeling program
that calculates the surface recession rate and interior temperature
distribution is necessary. Also, numerical solution of governing
equation for non-charring material ablation is presented in order to
anticipate the recession rate and the heat response of non-charring
heat shields. the governing equation is nonlinear and the Newton-
Rafson method along with TDMA algorithm is used to solve this
nonlinear equation system. Using Newton- Rafson method for
solving the governing equation is one of the advantages of the
solving method because this method is simple and it can be easily
generalized to more difficult problems. The obtained results
compared with reliable sources in order to examine the accuracy of
compiling code.
Abstract: Fluid flow and heat transfer of vertical full cone
embedded in porous media is studied in this paper. Nonlinear
differential equation arising from similarity solution of inverted cone
(subjected to wall temperature boundary conditions) embedded in
porous medium is solved using a hybrid neural network- particle
swarm optimization method.
To aim this purpose, a trial solution of the differential equation is
defined as sum of two parts. The first part satisfies the initial/
boundary conditions and does contain an adjustable parameter and
the second part which is constructed so as not to affect the
initial/boundary conditions and involves adjustable parameters (the
weights and biases) for a multi-layer perceptron neural network.
Particle swarm optimization (PSO) is applied to find adjustable
parameters of trial solution (in first and second part). The obtained
solution in comparison with the numerical ones represents a
remarkable accuracy.
Abstract: Wavelet transform has been extensively used in
machine fault diagnosis and prognosis owing to its strength to deal
with non-stationary signals. The existing Wavelet transform based
schemes for fault diagnosis employ wavelet decomposition of the
entire vibration frequency which not only involve huge
computational overhead in extracting the features but also increases
the dimensionality of the feature vector. This increase in the
dimensionality has the tendency to 'over-fit' the training data and
could mislead the fault diagnostic model. In this paper a novel
technique, envelope wavelet packet transform (EWPT) is proposed in
which features are extracted based on wavelet packet transform of the
filtered envelope signal rather than the overall vibration signal. It not
only reduces the computational overhead in terms of reduced number
of wavelet decomposition levels and features but also improves the
fault detection accuracy. Analytical expressions are provided for the
optimal frequency resolution and decomposition level selection in
EWPT. Experimental results with both actual and simulated machine
fault data demonstrate significant gain in fault detection ability by
EWPT at reduced complexity compared to existing techniques.
Abstract: The data is available in abundance in any business
organization. It includes the records for finance, maintenance,
inventory, progress reports etc. As the time progresses, the data keep
on accumulating and the challenge is to extract the information from
this data bank. Knowledge discovery from these large and complex
databases is the key problem of this era. Data mining and machine
learning techniques are needed which can scale to the size of the
problems and can be customized to the application of business. For
the development of accurate and required information for particular
problem, business analyst needs to develop multidimensional models
which give the reliable information so that they can take right
decision for particular problem. If the multidimensional model does
not possess the advance features, the accuracy cannot be expected.
The present work involves the development of a Multidimensional
data model incorporating advance features. The criterion of
computation is based on the data precision and to include slowly
change time dimension. The final results are displayed in graphical
form.
Abstract: Automatic Extraction of Event information from
social text stream (emails, social network sites, blogs etc) is a vital
requirement for many applications like Event Planning and
Management systems and security applications. The key information
components needed from Event related text are Event title, location,
participants, date and time. Emails have very unique distinctions over
other social text streams from the perspective of layout and format
and conversation style and are the most commonly used
communication channel for broadcasting and planning events.
Therefore we have chosen emails as our dataset. In our work, we
have employed two statistical NLP methods, named as Finite State
Machines (FSM) and Hidden Markov Model (HMM) for the
extraction of event related contextual information. An application
has been developed providing a comparison among the two methods
over the event extraction task. It comprises of two modules, one for
each method, and works for both bulk as well as direct user input.
The results are evaluated using Precision, Recall and F-Score.
Experiments show that both methods produce high performance and
accuracy, however HMM was good enough over Title extraction and
FSM proved to be better for Venue, Date, and time.
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: This paper studies stability of homogeneous beams
with piezoelectric layers subjected to axial load that is simply
supported at both ends lies on a continuous elastic foundation. The
displacement field of beam is assumed based on first order shear
deformation beam theory. Applying the Hamilton's principle, the
governing equation is established. The influences of applied voltage,
dimensionless geometrical parameter and foundation coefficient on
the stability of beam are presented. To investigate the accuracy of the
present analysis, a compression study is carried out with a known
data.
Abstract: In this paper, the local grid refinement is focused by
using a nested grid technique. The Cartesian grid numerical method is
developed for simulating unsteady, viscous, incompressible flows
with complex immersed boundaries. A finite volume method is used in
conjunction with a two-step fractional-step procedure. The key aspects
that need to be considered in developing such a nested grid solver are
imposition of interface conditions on the inter-block and accurate
discretization of the governing equation in cells that are with the
inter-block as a control surface. A new interpolation procedure is
presented which allows systematic development of a spatial
discretization scheme that preserves the spatial accuracy of the
underlying solver. The present nested grid method has been tested by
two numerical examples to examine its performance in the two
dimensional problems. The numerical examples include flow past a
circular cylinder symmetrically installed in a Channel and flow past
two circular cylinders with different diameters. From the numerical
experiments, the ability of the solver to simulate flows with
complicated immersed boundaries is demonstrated and the nested grid
approach can efficiently speed up the numerical solutions.
Abstract: The aged are faced with increasing risk for falls. The
aged have the easily fragile bones than others. When falls have
occurred, it is important to detect this emergency state because such
events often lead to more serious illness or even death. A
implementation of PDA system, for detection of emergency situation,
was developed using 3-axis accelerometer in this paper as follows.
The signals were acquired from the 3-axis accelerometer, and then
transmitted to the PDA through Bluetooth module. This system can
classify the human activity, and also detect the emergency state like
falls. When the fall occurs, the system generates the alarm on the
PDA. If a subject does not respond to the alarm, the system determines
whether the current situation is an emergency state or not, and then
sends some information to the emergency center in the case of urgent
situation. Three different studies were conducted on 12 experimental
subjects, with results indicating a good accuracy. The first study was
performed to detect the posture change of human daily activity. The
second study was performed to detect the correct direction of fall. The
third study was conducted to check the classification of the daily
physical activity. Each test was lasted at least 1 min. in third study.
The output of acceleration signal was compared and evaluated by
changing a various posture after attaching a 3-axis accelerometer
module on the chest. The newly developed system has some important
features such as portability, convenience and low cost. One of the
main advantages of this system is that it is available at home
healthcare environment. Another important feature lies in low cost to
manufacture device. The implemented system can detect the fall
accurately, so will be widely used in emergency situation.
Abstract: Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.
Abstract: This paper presents a novel approach to finding a
priori interesting regions in mammograms. In order to delineate those
regions of interest (ROI-s) in mammograms, which appear to be
prominent, a topographic representation called the iso-level contour
map consisting of iso-level contours at multiple intensity levels and
region segmentation based-thresholding have been proposed. The
simulation results indicate that the computed boundary gives the
detection rate of 99.5% accuracy.
Abstract: A New features are extracted and compared to
improve the prediction of protein-protein interactions. The basic idea
is to select and use the best set of features from the Tensor matrices
that are produced by the frequency vectors of the protein sequences.
Three set of features are compared, the first set is based on the
indices that are the most common in the interacting proteins, the
second set is based on the indices that tend to be common in the
interacting and non-interacting proteins, and the third set is
constructed by using random indices. Moreover, three encoding
strategies are compared; that are based on the amino asides polarity,
structure, and chemical properties. The experimental results indicate
that the highest accuracy can be obtained by using random indices
with chemical properties encoding strategy and support vector
machine.
Abstract: Nowadays use of a new structural bracing system
called 'Knee Bracing System' have taken the specialists attention too
much. On the other hand nonlinear static analysis procedures in
estimate structures performance in earthquake time have taken
attention too much. One of these procedure is modal pushover
analysis (MPA) procedure. The accuracy of MPA procedure for
simple steel moment resisting frame has been verified and considered
in Chintanapakdee and Chopra-s article in 2003. Since the accuracy
of MPA procedure has not verified for semi-rigid steel frames with
knee bracing, we are going to get through with this matter in this
study. For this purpose, the selected structures are four frames with
different heights, 5 to 20 stories, will be designed according to AISC
criteria. Then MPA procedure is used for the same frames with
different rigidity percentiles of connections. The results of seismic
responses are compared with dynamic nonlinear response history
analysis as exact procedure and accuracy of MPA procedure is
evaluated. It seems that MPA procedure accuracy will come down by
reduction of the rigidity percentiles of semi-rigid connections.
Abstract: A parallel computational fluid dynamics code has been
developed for the study of aerodynamic heating problem in hypersonic
flows. The code employs the 3D Navier-Stokes equations as the basic
governing equations to simulate the laminar hypersonic flow. The cell
centered finite volume method based on structured grid is applied for
spatial discretization. The AUSMPW+ scheme is used for the inviscid
fluxes, and the MUSCL approach is used for higher order spatial
accuracy. The implicit LU-SGS scheme is applied for time integration
to accelerate the convergence of computations in steady flows. A
parallel programming method based on MPI is employed to shorten
the computing time. The validity of the code is demonstrated by
comparing the numerical calculation result with the experimental data
of a hypersonic flow field around a blunt body.
Abstract: Understanding proteins functions is a major goal in
the post-genomic era. Proteins usually work in context of other
proteins and rarely function alone. Therefore, it is highly relevant to
study the interaction partners of a protein in order to understand its
function. Machine learning techniques have been widely applied to
predict protein-protein interactions. Kernel functions play an
important role for a successful machine learning technique. Choosing
the appropriate kernel function can lead to a better accuracy in a
binary classifier such as the support vector machines. In this paper,
we describe a Bayesian kernel for the support vector machine to
predict protein-protein interactions. The use of Bayesian kernel can
improve the classifier performance by incorporating the probability
characteristic of the available experimental protein-protein
interactions data that were compiled from different sources. In
addition, the probabilistic output from the Bayesian kernel can assist
biologists to conduct more research on the highly predicted
interactions. The results show that the accuracy of the classifier has
been improved using the Bayesian kernel compared to the standard
SVM kernels. These results imply that protein-protein interaction can
be predicted using Bayesian kernel with better accuracy compared to
the standard SVM kernels.
Abstract: For high-speed control of robots, a good knowledge of system modelling is necessary to obtain the desired bandwidth. In this paper, we present a cartesian robot with a pan/tilt unit in end-effector (5 dof). This robot is implemented with powerful direct drive AC induction machines. The dynamic model, parameter identification and model validation of the robot are studied (including actuators). This work considers the cartesian robot coupled and non linear (contrary to normal considerations for this type of robots). The mechanical and control architecture proposed in this paper is efficient for industrial and research application in which high speed, well known model and very high accuracy are required.
Abstract: A new approach for timestamp ordering problem in
serializable schedules is presented. Since the number of users using
databases is increasing rapidly, the accuracy and needing high
throughput are main topics in database area. Strict 2PL does not
allow all possible serializable schedules and so does not result high
throughput. The main advantages of the approach are the ability to
enforce the execution of transaction to be recoverable and the high
achievable performance of concurrent execution in central databases.
Comparing to Strict 2PL, the general structure of the algorithm is
simple, free deadlock, and allows executing all possible serializable
schedules which results high throughput. Various examples which
include different orders of database operations are discussed.
Abstract: Brain ArterioVenous Malformation (BAVM) is an abnormal tangle of brain blood vessels where arteries shunt directly into veins with no intervening capillary bed which causes high pressure and hemorrhage risk. The success of treatment by embolization in interventional neuroradiology is highly dependent on the accuracy of the vessels visualization. In this paper the performance of clustering techniques on vessel segmentation from 3- D rotational angiography (3DRA) images is investigated and a new technique of segmentation is proposed. This method consists in: preprocessing step of image enhancement, then K-Means (KM), Fuzzy C-Means (FCM) and Expectation Maximization (EM) clustering are used to separate vessel pixels from background and artery pixels from vein pixels when possible. A post processing step of removing false-alarm components is applied before constructing a three-dimensional volume of the vessels. The proposed method was tested on six datasets along with a medical assessment of an expert. Obtained results showed encouraging segmentations.
Abstract: This paper discusses applications of a revolutionary
information technology, Geographic Information Systems (GIS), in
the field of the history of cartography by examples, including
assessing accuracy of early maps, establishing a database of places
and historical administrative units in history, integrating early maps
in GIS or digital images, and analyzing social, political, and
economic information related to production of early maps. GIS
provides a new mean to evaluate the accuracy of early maps. Four
basic steps using GIS for this type of study are discussed. In addition,
several historical geographical information systems are introduced.
These include China Historical Geographic Information Systems
(CHGIS), the United States National Historical Geographic
Information System (NHGIS), and the Great Britain Historical
Geographical Information System. GIS also provides digital means to
display and analyze the spatial information on the early maps or to
layer them with modern spatial data. How GIS relational data
structure may be used to analyze social, political, and economic
information related to production of early maps is also discussed in
this paper. Through discussion on these examples, this paper reveals
value of GIS applications in this field.
Abstract: This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.