Abstract: Intrusion detection is a mechanism used to protect a
system and analyse and predict the behaviours of system users. An
ideal intrusion detection system is hard to achieve due to
nonlinearity, and irrelevant or redundant features. This study
introduces a new anomaly-based intrusion detection model. The
suggested model is based on particle swarm optimisation and
nonlinear, multi-class and multi-kernel support vector machines.
Particle swarm optimisation is used for feature selection by applying
a new formula to update the position and the velocity of a particle;
the support vector machine is used as a classifier. The proposed
model is tested and compared with the other methods using the KDD
CUP 1999 dataset. The results indicate that this new method achieves
better accuracy rates than previous methods.
Abstract: Earthquakes are natural phenomena that occur with influence of a lot of parameters such as seismic activity, changing in the ground waters' motion, changing in the water-s temperature, etc. On the other hand, the radon gas concentrations in soil vary as nonlinear generally with earthquakes. Continuous measurement of the soil radon gas is very important for determination of characteristic of the seismic activity. The radon gas changes as continuous with strain occurring within the Earth-s surface during an earthquake and effects from the physical and the chemical processes such as soil structure, soil permeability, soil temperature, the barometric pressure, etc. Therefore, at the modeling researches are notsufficient to knowthe concentration ofradon gas. In this research, we determined relationships between radon emissions based on the environmental parameters and earthquakes occurring along the East Anatolian Fault Zone (EAFZ), Turkiye and predicted magnitudes of some earthquakes with the artificial neural network (ANN) model.
Abstract: Defense and Aerospace environment is continuously
striving to keep up with increasingly sophisticated Information
Technology (IT) in order to remain effective in today-s dynamic and
unpredictable threat environment. This makes IT one of the largest
and fastest growing expenses of Defense. Hundreds of millions of
dollars spent a year on IT projects. But, too many of those millions
are wasted on costly mistakes. Systems that do not work properly,
new components that are not compatible with old ones, trendy new
applications that do not really satisfy defense needs or lost through
poorly managed contracts.
This paper investigates and compiles the effective strategies that
aim to end exasperation with low returns and high cost of
Information Technology acquisition for defense; it tries to show how
to maximize value while reducing time and expenditure.
Abstract: The performance of Advection Upstream Splitting
Method AUSM schemes are evaluated against experimental flow
fields at different Mach numbers and results are compared with
experimental data of subsonic, supersonic and hypersonic flow fields.
The turbulent model used here is SST model by Menter. The
numerical predictions include lift coefficient, drag coefficient and
pitching moment coefficient at different mach numbers and angle of
attacks. This work describes a computational study undertaken to
compute the Aerodynamic characteristics of different air vehicles
configurations using a structured Navier-Stokes computational
technique. The CFD code bases on the idea of upwind scheme for the
convective (convective-moving) fluxes. CFD results for GLC305
airfoil and cone cylinder tail fined missile calculated on above
mentioned turbulence model are compared with the available data.
Wide ranges of Mach number from subsonic to hypersonic speeds are
simulated and results are compared. When the computation is done
by using viscous turbulence model the above mentioned coefficients
have a very good agreement with the experimental values. AUSM
scheme is very efficient in the regions of very high pressure gradients
like shock waves and discontinuities. The AUSM versions simulate
the all types of flows from lower subsonic to hypersonic flow without
oscillations.
Abstract: Thermal conductivity is an important characteristic of
a nanofluid in laminar flow heat transfer. This paper presents an
improved model for the prediction of the effective thermal
conductivity of nanofluids based on dimensionless groups. The
model expresses the thermal conductivity of a nanofluid as a function
of the thermal conductivity of the solid and liquid, their volume
fractions and particle size. The proposed model includes a parameter
which accounts for the interfacial shell, brownian motion, and
aggregation of particle. The validation of the model is verified by
applying the results obtained by the experiments of Tio2-water and
Al2o3-water nanofluids.
Abstract: A two-parameter fatigue model explicitly accounting for the cyclic as well as the mean stress was used to fit static and fatigue data available in literature concerning carbon fiber reinforced composite laminates subjected tension-tension fatigue. The model confirms the strength–life equal rank assumption and predicts reasonably the probability of failure under cyclic loading. The model parameters were found by best fitting procedures and required a minimum of experimental tests.
Abstract: In this paper back-propagation artificial neural
network (BPANN) is employed to predict the limiting drawing ratio
(LDR) of the deep drawing process. To prepare a training set for
BPANN, some finite element simulations were carried out. die and
punch radius, die arc radius, friction coefficient, thickness, yield
strength of sheet and strain hardening exponent were used as the
input data and the LDR as the specified output used in the training of
neural network. As a result of the specified parameters, the program
will be able to estimate the LDR for any new given condition.
Comparing FEM and BPANN results, an acceptable correlation was
found.
Abstract: State-of-the-art methods for secondary structure (Porter, Psi-PRED, SAM-T99sec, Sable) and solvent accessibility (Sable, ACCpro) predictions use evolutionary profiles represented by the position specific scoring matrix (PSSM). It has been demonstrated that evolutionary profiles are the most important features in the feature space for these predictions. Unfortunately applying PSSM matrix leads to high dimensional feature spaces that may create problems with parameter optimization and generalization. Several recently published suggested that applying feature extraction for the PSSM matrix may result in improvements in secondary structure predictions. However, none of the top performing methods considered here utilizes dimensionality reduction to improve generalization. In the present study, we used simple and fast methods for features selection (t-statistics, information gain) that allow us to decrease the dimensionality of PSSM matrix by 75% and improve generalization in the case of secondary structure prediction compared to the Sable server.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: This paper discusses a systematic design of a Σ-Δ fractional-N Phase-Locked Loop based on HDL behavioral modeling. The proposed design consists in describing the mixed behavior of this PLL architecture starting from the specifications of each building block. The HDL models of critical PLL blocks have been described in VHDL-AMS to predict the different specifications of the PLL. The effect of different noise sources has been efficiently introduced to study the PLL system performances. The obtained results are compared with transistor-level simulations to validate the effectiveness of the proposed models for wireless applications in the frequency range around 2.45 GHz.
Abstract: In recent years, global warming has become a
worldwide problem. The reduction of carbon dioxide emissions is a
top priority for many companies in the manufacturing industry. In the
automobile industry as well, the reduction of carbon dioxide emissions
is one of the most important issues. Technology to reduce the weight
of automotive parts improves the fuel economy of automobiles, and is
an important technology for reducing carbon dioxide. Also, even if
this weight reduction technology is applied to electric automobiles
rather than gasoline automobiles, reducing energy consumption
remains an important issue. Plastic processing of hollow pipes is one
important technology for realizing the weight reduction of automotive
parts. Ohashi et al. [1],[2] present an example of research on pipe
formation in which a process was carried out to enlarge a pipe
diameter using a lost core, achieving the suppression of wall thickness
reduction and greater pipe expansion than hydroforming.
In this study, we investigated a method to increase the wall
thickness of a pipe through pipe compression using planetary rolls.
The establishment of a technology whereby the wall thickness of a
pipe can be controlled without buckling the pipe is an important
technology for the weight reduction of products. Using the finite
element analysis method, we predicted that it would be possible to
increase the compression of an aluminum pipe with a 3mm wall
thickness by approximately 20%, and wall thickness by approximately
20% by pressing the hollow pipe with planetary rolls.
Abstract: For many industrial applications plate heat
exchangers are demonstrating a large superiority over the
other types of heat exchangers. The efficiency of such a
device depends on numerous factors the effect of which needs
to be analysed and accurately evaluated.
In this paper we present a theoretical analysis of a cocurrent
plate heat exchanger and the results of its numerical
simulation.
Knowing the hot and the cold fluid streams inlet temperatures,
the respective heat capacities mCp
and the value of the
overall heat transfer coefficient, a 1-D mathematical model
based on the steady flow energy balance for a differential
length of the device is developed resulting in a set of N first
order differential equations with boundary conditions where N
is the number of channels.For specific heat exchanger
geometry and operational parameters, the problem is
numerically solved using the shooting method.
The simulation allows the prediction of the temperature
map in the heat exchanger and hence, the evaluation of its
performances. A parametric analysis is performed to evaluate
the influence of the R-parameter on the e-NTU values. For
practical purposes effectiveness-NTU graphs are elaborated
for specific heat exchanger geometry and different operating
conditions.
Abstract: New Zealand-s product markets experienced a surge in
import competition beginning from the late 1970-s when its
government began to promote a policy of more open markets. This
study considers how the trade liberalization aspect of the policy may
have influenced unionization and union-organizing success. For
describing the trade liberalization, a model shows how the removal of
import tariffs can lead to countervailing influences upon the union
membership of a domestic firm. The evidence supports the prediction
that union membership has been decreased rather than increased. In
the context of debates concerning globalization, it can be said that the
power of unions has been diminished.
Abstract: Middle-gate is located in Hasankeyf, Batman dating
back to 1800 BC and is one of the important historical structures in
Turkey. The ancient structure has suffered major structural cracks
due to aging as well as lateral pressure of a cracked rock which is
predicted to be about 100 tons. The existing support system was
found to be inadequate to support the load especially after a recent
rock fall in the close vicinity. Concerns were increased since the
existing support system that is integral with a damaged and cracked
gate wall needed to be replaced by a new support system. The
replacement process must be carefully monitored by crackmeters and
control mechanisms should be integrated to prevent cracks to expand
while the same crack width needs to be maintained after the
operation. The control system and actions taken during the
intervention are explained in this paper.
Abstract: Protein subchloroplast locations are correlated with its
functions. In contrast to the large amount of available protein
sequences, the information of their locations and functions is less
known. The experiment works for identification of protein locations
and functions are costly and time consuming. The accurate prediction
of protein subchloroplast locations can accelerate the study of
functions of proteins in chloroplast. This study proposes a Random
Forest based method, ChloroRF, to predict protein subchloroplast
locations using interpretable physicochemical properties. In addition
to high prediction accuracy, the ChloroRF is able to select important
physicochemical properties. The important physicochemical
properties are also analyzed to provide insights into the underlying
mechanism.
Abstract: This paper addresses the problem of how one can
improve the performance of a non-optimal filter. First the theoretical question on dynamical representation for a given time correlated
random process is studied. It will be demonstrated that for a wide class of random processes, having a canonical form, there exists
a dynamical system equivalent in the sense that its output has the
same covariance function. It is shown that the dynamical approach is more effective for simulating and estimating a Markov and non-
Markovian random processes, computationally is less demanding,
especially with increasing of the dimension of simulated processes.
Numerical examples and estimation problems in low dimensional
systems are given to illustrate the advantages of the approach. A very useful application of the proposed approach is shown for the
problem of state estimation in very high dimensional systems. Here a modified filter for data assimilation in an oceanic numerical model
is presented which is proved to be very efficient due to introducing
a simple Markovian structure for the output prediction error process
and adaptive tuning some parameters of the Markov equation.
Abstract: Surveillance system is widely used in the traffic
monitoring. The deployment of cameras is moving toward a
ubiquitous camera (UbiCam) environment. In our previous study, a
novel service, called GPS-VT, was firstly proposed by incorporating
global positioning system (GPS) and visual tracking techniques for
the UbiCam environment. The first prototype is called GODTA
(GPS-based Moving Object Detection and Tracking Approach). For a
moving person carried GPS-enabled mobile device, he can be
tracking when he enters the field-of-view (FOV) of a camera
according to his real-time GPS coordinate. In this paper, GPS-VT
service is applied to the tracking of vehicles. The moving speed of a
vehicle is much faster than a person. It means that the time passing
through the FOV is much shorter than that of a person. Besides, the
update interval of GPS coordinate is once per second, it is
asynchronous with the frame rate of the real-time image. The above
asynchronous is worsen by the network transmission delay. These
factors are the main challenging to fulfill GPS-VT service on a
vehicle.In order to overcome the influence of the above factors, a
back-propagation neural network (BPNN) is used to predict the
possible lane before the vehicle enters the FOV of a camera. Then, a
template matching technique is used for the visual tracking of a target
vehicle. The experimental result shows that the target vehicle can be
located and tracking successfully. The success location rate of the
implemented prototype is higher than that of the previous GODTA.
Abstract: The groundwater is one of the main sources for
sustainability in the United Arab Emirates (UAE). Intensive
developments in Al-Ain area lead to increase water demand, which
consequently reduced the overall groundwater quantity in major
aquifers. However, in certain residential areas within Al-Ain, it has
been noticed that the groundwater level is rising, for example in
Sha-ab Al Askher area. The reasons for the groundwater rising
phenomenon are yet to be investigated. In this work, twenty four
seismic refraction profiles have been carried out along the study
pilot area; as well as field measurement of the groundwater level in
a number of available water wells in the area. The processed
seismic data indicated the deepest and shallowest groundwater
levels are 15m and 2.3 meters respectively. This result is greatly
consistent with the proper field measurement of the groundwater
level. The minimum detected value may be referred to perched
subsurface water which may be associated to the infiltration from
the surrounding water bodies such as lakes, and elevated farms. The
maximum values indicate the accurate groundwater level within the
study area. The findings of this work may be considered as a
preliminary help to the decision makers.
Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.