Abstract: In the present paper, Fatigue life assessment of an
anti-roll bar component of a passenger vehicle, is investigated by
ANSYS 11 software. A stress analysis is also carried out by the
finite element technique for the determination of highly stressed
regions on the bar. Anti-roll bar is a suspension element used at the
front, rear, or at both ends of a car that reduces body roll by resisting
any unequal vertical motion between the pair of wheels to which it is
connected. As a first stage, fatigue damage models proposed by some
well-known references and the corresponding assumptions are
discussed and some enhancements are proposed. Then, fracture
analysis of an anti-roll bar of an automobile is carried out. The
analysed type of the anti-roll bar is especially important as many
cases are reported about the fracture after a 100,000 km of travel
fatigue and fracture conditions. This paper demonstrates fatigue life
of an anti-roll bar and then evaluated by experimental analytically
results from other researcher.
Abstract: When considering the development of constitutive
equations describing the behavior of materials under cyclic plastic
strains, different kinds of formulations can be adopted. The primary
intention of this study is to develop computer programming of
plasticity models to accurately predict the life of engineering
components. For this purpose, the energy or cyclic strain is computed
in multi-surface plasticity models in non-proportional loading and to
present their procedures and codes results.
Abstract: A new Feed-Forward/Feedback Generalized
Minimum Variance Pole-placement Controller to incorporate the
robustness of classical pole-placement into the flexibility of
generalized minimum variance self-tuning controller for Single-Input
Single-Output (SISO) has been proposed in this paper. The design,
which provides the user with an adaptive mechanism, which ensures
that the closed loop poles are, located at their pre-specified positions.
In addition, the controller design which has a feed-forward/feedback
structure overcomes the certain limitations existing in similar poleplacement
control designs whilst retaining the simplicity of
adaptation mechanisms used in other designs. It tracks set-point
changes with the desired speed of response, penalizes excessive
control action, and can be applied to non-minimum phase systems.
Besides, at steady state, the controller has the ability to regulate the
constant load disturbance to zero. Example simulation results using
both simulated and real plant models demonstrate the effectiveness of
the proposed controller.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
Abstract: In this paper, we demonstrate the adaptive
least-mean-square (LMS) filter modeling using SystemC. SystemC is
a modeling language that allows designer to model both hardware and
software component and makes it possible to design from high level
system of abstraction to low level system of abstraction. We produced
five adaptive least-mean-square filter models that are classed as five
abstraction levels using SystemC proceeding from the abstract model
to the more concrete model.
Abstract: A numerical study of flow in a horizontally channel
partially filled with a porous screen with non-uniform inlet has been
performed by lattice Boltzmann method (LBM). The flow in porous
layer has been simulated by the Brinkman-Forchheimer model.
Numerical solutions have been obtained for variable porosity models
and the effects of Darcy number and porosity have been studied in
detail. It is found that the flow stabilization is reliant on the Darcy
number. Also the results show that the stabilization of flow field and
heat transfer is depended to Darcy number. Distribution of stream
field becomes more stable by decreasing Darcy number. Results
illustrate that the effect of variable porosity is significant just in the
region of the solid boundary. In addition, difference between constant
and variable porosity models is decreased by decreasing the Darcy
number.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: Parametric models have been quite popular for
studying human growth, particularly in relation to biological
parameters such as peak size velocity and age at peak size velocity.
Longitudinal data are generally considered to be vital for fittinga
parametric model to individual-specific data, and for studying the
distribution of these biological parameters in a human population.
However, cross-sectional data are easier to obtain than longitudinal
data. In this paper, we present a method of combining longitudinal
and cross-sectional data for the purpose of estimating the distribution
of the biological parameters. We demonstrate, through simulations in
the special case ofthePreece Baines model, how estimates based on
longitudinal data can be improved upon by harnessing the
information contained in cross-sectional data.We study the extent of
improvement for different mixes of the two types of data, and finally
illustrate the use of the method through data collected by the Indian
Statistical Institute.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.
Abstract: Although the STL (stereo lithography) file format is
widely used as a de facto industry standard in the rapid prototyping
industry due to its simplicity and ability to tessellation of almost all
surfaces, but there are always some defects and shortcoming in their
usage, which many of them are difficult to correct manually. In
processing the complex models, size of the file and its defects grow
extremely, therefore, correcting STL files become difficult. In this
paper through optimizing the exiting algorithms, size of the files and
memory usage of computers to process them will be reduced. In spite
of type and extent of the errors in STL files, the tail-to-head
searching method and analysis of the nearest distance between tails
and heads techniques were used. As a result STL models sliced
rapidly, and fully closed contours produced effectively and errorless.
Abstract: Using a set of confidence intervals, we develop a
common approach, to construct a fuzzy set as an estimator for
unknown parameters in statistical models. We investigate a method
to derive the explicit and unique membership function of such fuzzy
estimators. The proposed method has been used to derive the fuzzy
estimators of the parameters of a Normal distribution and some
functions of parameters of two Normal distributions, as well as the
parameters of the Exponential and Poisson distributions.
Abstract: The growing outsourcing of logistics services
resulting from the ongoing current in firms of costs
reduction/increased efficiency means that it is becoming more and
more important for the companies doing the outsourcing to carry out
a proper evaluation.
The multiple definitions and measures of logistics service
performance found in research on the topic create a certain degree of
confusion and do not clear the way towards the proper measurement
of their performance. Do a model and a specific set of indicators exist
that can be considered appropriate for measuring the performance of
logistics services outsourcing in industrial environments? Are said
indicators in keeping with the objectives pursued by outsourcing? We
aim to answer these and other research questions in the study we have
initiated in the field within the framework of the international High
Performance Manufacturing (HPM) project of which this paper
forms part.
As the first stage of this research, this paper reviews articles
dealing with the topic published in the last 15 years with the aim of
detecting the models most used to make this measurement and
determining which performance indicators are proposed as part of
said models and which are most used. The first steps are also taken in
determining whether these indicators, financial and operational, cover
the aims that are being pursued when outsourcing logistics services.
The findings show there is a wide variety of both models and
indicators used. This would seem to testify to the need to continue
with our research in order to try to propose a model and a set of
indicators for measuring the performance of logistics services
outsourcing in industrial environments.
Abstract: This paper describes a 3D modeling system in
Augmented Reality environment, named 3DARModeler. It can be
considered a simple version of 3D Studio Max with necessary
functions for a modeling system such as creating objects, applying
texture, adding animation, estimating real light sources and casting
shadows. The 3DARModeler introduces convenient, and effective
human-computer interaction to build 3D models by combining both
the traditional input method (mouse/keyboard) and the tangible input
method (markers). It has the ability to align a new virtual object with
the existing parts of a model. The 3DARModeler targets nontechnical
users. As such, they do not need much knowledge of
computer graphics and modeling techniques. All they have to do is
select basic objects, customize their attributes, and put them together
to build a 3D model in a simple and intuitive way as if they were
doing in the real world. Using the hierarchical modeling technique,
the users are able to group several basic objects to manage them as a
unified, complex object. The system can also connect with other 3D
systems by importing and exporting VRML/3Ds Max files. A
module of speech recognition is included in the system to provide
flexible user interfaces.
Abstract: The birdhouses and dovecotes, which are the indicator
of naturalness and human-animal relationship, are one of the
traditional cultural values of Turkey. With their structures compatible
with nature and respectful to humans the bird houses and dovecotes,
which have an important position in local urbanization models as a
representative of the civil architecture with their unique form and
function are important subjects that should be evaluated in a wide
frame comprising from architecture to urbanism, from ecologic
agriculture to globalization. The traditional bird houses and
dovecotes are disregarded due to the insensitivity affecting the city
life and the change in the public sense of art. In this study, the
characteristic properties of traditional dovecotes and birdhouses,
started in 13th century and ended in 19th century in Anatolia, are
tried to be defined for the sustainability of the tradition and for giving
a new direction to the designers.
Abstract: The paper attempts to contribute to the largely
neglected social and anthropological discussion of technology development on the one hand, and to redirecting the emphasis in
anthropology from primitive and exotic societies to problems of high
relevance in contemporary era and how technology is used in
everyday life. It draws upon multidimensional models of intelligence
and ideal type formation. It is argued that the predominance of
computational and cognitive cosmovisions have led to technology alienation. Injection of communicative competence in artificially
intelligent systems and identity technologies in the coming
information society are analyzed
Abstract: The paper evaluates several hundred one-day-ahead
VaR forecasting models in the time period between the years 2004
and 2009 on data from six world stock indices - DJI, GSPC, IXIC,
FTSE, GDAXI and N225. The models model mean using the ARMA
processes with up to two lags and variance with one of GARCH,
EGARCH or TARCH processes with up to two lags. The models are
estimated on the data from the in-sample period and their forecasting
accuracy is evaluated on the out-of-sample data, which are more
volatile. The main aim of the paper is to test whether a model
estimated on data with lower volatility can be used in periods with
higher volatility. The evaluation is based on the conditional coverage
test and is performed on each stock index separately. The primary
result of the paper is that the volatility is best modelled using a
GARCH process and that an ARMA process pattern cannot be found
in analyzed time series.
Abstract: In this project cadmium ions were adsorbed from
aqueous solutions onto either date pits; a cheap agricultural and nontoxic
material, or chemically activated carbon prepared from date pits
using phosphoric acid. A series of experiments were conducted in a
batch adsorption technique to assess the feasibility of using the
prepared adsorbents. The effects of the process variables such as
initial cadmium ions concentration, contact time, solution pH and
adsorbent dose on the adsorption capacity of both adsorbents were
studied. The experimental data were tested using different isotherm
models such as Langmuir, Freundlich, Tempkin and Dubinin-
Radushkevich. The results showed that although the equilibrium data
could be described by all models used, Langmuir model gave slightly
better results when using activated carbon while Freundlich model,
gave better results with date pits.
Abstract: Today air-core coils (ACC) are a viable alternative to
ferrite-core coils in a range of applications due to their low induction
effect. An analytical study was carried out and the results were used as
a guide to understand the relationship between the magnet-coil
distance and the resulting attractive magnetic force. Four different
ACC models were fabricated for experimental study. The variation in
the models included the dimensions, the number of coil turns and the
current supply to the coil. Comparison between the analytical and
experimental results for all the models shows an average discrepancy
of less than 10%. An optimized ACC design was selected for the
scanner which can provide maximum magnetic force.