Abstract: In this paper, we have presented the effect of varying
time-delays on performance and stability in the single-channel multirate
sampled-data system in hard real-time (RT-Linux) environment.
The sampling task require response time that might exceed the
capacity of RT-Linux. So a straight implementation with RT-Linux is
not feasible, because of the latency of the systems and hence,
sampling period should be less to handle this task. The best sampling
rate is chosen for the sampled-data system, which is the slowest rate
meets all performance requirements. RT-Linux is consistent with its
specifications and the resolution of the real-time is considered 0.01
seconds to achieve an efficient result. The test results of our
laboratory experiment shows that the multi-rate control technique in
hard real-time operating system (RTOS) can improve the stability
problem caused by the random access delays and asynchronization.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: Robust face recognition under various illumination
environments is very difficult and needs to be accomplished for
successful commercialization. In this paper, we propose an improved
illumination normalization method for face recognition. Illumination
normalization algorithm based on anisotropic smoothing is well known
to be effective among illumination normalization methods but
deteriorates the intensity contrast of the original image, and incurs less
sharp edges. The proposed method in this paper improves the previous
anisotropic smoothing-based illumination normalization method so
that it increases the intensity contrast and enhances the edges while
diminishing the effect of illumination variations. Due to the result of
these improvements, face images preprocessed by the proposed
illumination normalization method becomes to have more distinctive
feature vectors (Gabor feature vectors) for face recognition. Through
experiments of face recognition based on Gabor feature vector
similarity, the effectiveness of the proposed illumination
normalization method is verified.
Abstract: Natural frequencies and dynamic response of a spur
gear sector are investigated using a two dimensional finite element
model that offers significant advantages for dynamic gear analyses.
The gear teeth are analyzed for different operating speeds. A primary
feature of this modeling is determination of mesh forces using a
detailed contact analysis for each time step as the gears roll through
the mesh. ANSYS software has been used on the proposed model to
find the natural frequencies by Block Lanczos technique and
displacements and dynamic stresses by transient mode super position
method. The effect of rotational speed of the gear on the dynamic
response of gear tooth has been studied and design limits have been
discussed.
Abstract: There are many real world problems in which
parameters like the arrival time of new jobs, failure of resources, and
completion time of jobs change continuously. This paper tackles the
problem of scheduling jobs with random due dates on multiple
identical machines in a stochastic environment. First to assign jobs to
different machine centers LPT scheduling methods have been used,
after that the particular sequence of jobs to be processed on the
machine have been found using simple stochastic techniques. The
performance parameter under consideration has been the maximum
lateness concerning the stochastic due dates which are independent
and exponentially distributed. At the end a relevant problem has been
solved using the techniques in the paper..
Abstract: The optimization problem using time scales is studied.
Time scale is a model of time. The language of time scales seems to
be an ideal tool to unify the continuous-time and the discrete-time
theories. In this work we present necessary conditions for a solution
of an optimization problem on time scales. To obtain that result we
use properties and results of the partial diamond-alpha derivatives for
continuous-multivariable functions. These results are also presented
here.
Abstract: In this paper, we explore the applicability of the Sinc-
Collocation method to a three-dimensional (3D) oceanography model.
The model describes a wind-driven current with depth-dependent
eddy viscosity in the complex-velocity system. In general, the
Sinc-based methods excel over other traditional numerical methods
due to their exponentially decaying errors, rapid convergence and
handling problems in the presence of singularities in end-points.
Together with these advantages, the Sinc-Collocation approach that
we utilize exploits first derivative interpolation, whose integration
is much less sensitive to numerical errors. We bring up several
model problems to prove the accuracy, stability, and computational
efficiency of the method. The approximate solutions determined by
the Sinc-Collocation technique are compared to exact solutions and
those obtained by the Sinc-Galerkin approach in earlier studies. Our
findings indicate that the Sinc-Collocation method outperforms other
Sinc-based methods in past studies.
Abstract: The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: The γ-turns play important roles in protein folding and
molecular recognition. The prediction and analysis of γ-turn types are
important for both protein structure predictions and better
understanding the characteristics of different γ-turn types. This study
proposed a physicochemical property-based decision tree (PPDT)
method to interpretably predict γ-turn types. In addition to the good
prediction performance of PPDT, three simple and human
interpretable IF-THEN rules are extracted from the decision tree
constructed by PPDT. The identified informative physicochemical
properties and concise rules provide a simple way for discriminating
and understanding γ-turn types.
Abstract: The amounts of radioactivity in the igneous rocks
have been investigated; samples were collected from the total of eight
basalt rock types in the northeastern of Kurdistan region/Iraq. The
activity concentration of 226Ra (238U) series, 228Ac (232Th) series, 40K
and 137Cs were measured using Planar HPGe and NaI(Tl) detectors.
Along the study area the radium equivalent activities Raeq in Bq/Kg
of samples under investigation were found in the range of 22.16 to
77.31 Bq/Kg with an average value of 44.8 Bq/Kg, this value is much
below the internationally accepted value of 370 Bq/Kg. To estimate
the health effects of this natural radioactive composition, the average
values of absorbed gamma dose rate D (55 nGyh-1), Indoor and
outdoor annual effective dose rates Eied (0.11 mSvy-1) . and Eoed
(0.03 mSvy-1), External hazard index Hex (0.138) and internal hazard
index Hin(0.154), and representative level index Iγr (0.386) have been
calculated and found to be lower than the worldwide average values.
Abstract: In order to improve the effect of isolation structure, the
principles and behaviours of the base-isolation system are studied, and
the types and characteristics of the base-isolation are also discussed.
Compared to the traditional aseismatic structures, the base isolation
structures decrease the seismic response obviously: the total structural
aseismatic value decreases to 1/4-1/32 and the seismic shear stress in
the upper structure decreases to 1/14-1/23. In the huge seism, the
structure can have an obvious aseismatic effect.
Abstract: Socioeconomic stability and development of a
country, can be describe by housing affordability. It is aimed to
ensure the housing provided as one of the key factors that is
affordable by every income earner group whether low-income,
middle income and high income group. This research carried out is to
find out affordability of home ownership level for first medium cost
landed-house by the middle-income group in Selangor, Malaysia. It is
also hope that it could be seen as able to contribute to the knowledge
and understanding on housing affordability level for the middleincome
group and variables that influenced the medium income
group-s ability to own first medium-cost houses.
Abstract: The network traffic data provided for the design of
intrusion detection always are large with ineffective information and
enclose limited and ambiguous information about users- activities.
We study the problems and propose a two phases approach in our
intrusion detection design. In the first phase, we develop a
correlation-based feature selection algorithm to remove the worthless
information from the original high dimensional database. Next, we
design an intrusion detection method to solve the problems of
uncertainty caused by limited and ambiguous information. In the
experiments, we choose six UCI databases and DARPA KDD99
intrusion detection data set as our evaluation tools. Empirical studies
indicate that our feature selection algorithm is capable of reducing the
size of data set. Our intrusion detection method achieves a better
performance than those of participating intrusion detectors.
Abstract: Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.
Abstract: Chest pain is one of the most prevalent complaints
among adults that cause the people to attend to medical centers. The
aim was to determine the prevalence and risk factors of chest pain
among over 30 years old people in Tehran. In this cross-sectional
study, 787 adults took part from Apr 2005 until Apr 2006. The
sampling method was random cluster sampling and there were 25
clusters. In each cluster, interviews were performed with 32 over 30
years old, people lived in those houses. In cases with chest pain, extra
questions asked. The prevalence of CP was 9% (71 cases). Of them
21 cases (6.5%) were in 41-60 year age ranges and the remainders
were over 61 year old. 19 cases (26.8%) mentioned CP in resting
state and all of the cases had exertion onset CP. The CP duration was
10 minutes or less in all of the cases and in most of them (84.5%), the
location of pain mentioned left anterior part of chest, left anterior part
of sternum and or left arm. There was positive history of myocardial
infarction in 12 cases (17%). There was significant relation between
CP and age, sex and between history of myocardial infarction and
marital state of study people. Our results are similar to other studies-
results in most parts, however it is necessary to perform
supplementary tests and follow up studies to differentiate between
cardiac and non-cardiac CP exactly.
Abstract: To fight against the economic crisis, French
Government, like many others in Europe, has decided to give a boost
to high-speed line projects. This paper explores the implementation
and decision-making process in TGV projects, their evolutions,
especially since the Mediterranean TGV-line. This project was
probably the most controversial, but paradoxically represents today a
huge success for all the actors involved.
What kind of lessons we can learn from this experience? How to
evaluate the impact of this project on TGV-line planning? How can
we characterize this implementation and decision-making process
regards to the sustainability challenges?
The construction of Mediterranean TGV-line was the occasion to
make several innovations: to introduce more dialog into the decisionmaking
process, to take into account the environment, to introduce a
new project management and technological innovations. That-s why
this project appears today as an example in terms of integration of
sustainable development.
In this paper we examine the different kinds of innovations
developed in this project, by using concepts from sociology of
innovation to understand how these solutions emerged in a
controversial situation. Then we analyze the lessons which were
drawn from this decision-making process (in the immediacy and a
posteriori) and the way in which procedures evolved: creation of new
tools and devices (public consultation, project management...).
Finally we try to highlight the impact of this evolution on TGV
projects governance. In particular, new methods of implementation
and financing involve a reconfiguration of the system of actors. The
aim of this paper is to define the impact of this reconfiguration on
negotiations between stakeholders.
Abstract: The results of the two-phase gas-solid jet in pneumatic
powder injection process analysis were presented in the paper. The
researches were conducted on model set-up with high speed camera
jet movement recording. Then the recorded material was analyzed to
estimate main particles movement parameters. The values obtained
from this direct measurement were compared to those calculated with
the use of the well-known formulas for the two-phase flows
(pneumatic conveying). Moreover, they were compared to
experimental results previously achieved by authors. The analysis led
to conclusions which to some extent changed the assumptions used
even by authors, regarding the two-phase jet in pneumatic powder
injection process. Additionally, the visual analysis of the recorded
clips supplied data to make a more complete evaluation of the jet
behavior in the lance outlet than before.
Abstract: Deaminated lesions were produced via nitrosative oxidation of natural nucleobases; uracul (Ura, U) from cytosine (Cyt, C), hypoxanthine (Hyp, H) from adenine (Ade, A), and xanthine (Xan, X) and oxanine (Oxa, O) from guanine (Gua, G). Such damaged nucleobases may induce mutagenic problems, so that much attentions and efforts have been poured on the revealing of their mechanisms in vivo or in vitro. In this study, we employed these deaminated lesions as useful probes for analysis of DNA-binding/recognizing proteins or enzymes. Since the pyrimidine lesions such as Hyp, Oxa and Xan are employed as analogues of guanine, their comparative uses are informative for analyzing the role of Gua in DNA sequence in DNA-protein interaction. Several DNA oligomers containing such Hyp, Oxa or Xan substituted for Gua were designed to reveal the molecular interaction between DNA and protein. From this approach, we have got useful information to understand the molecular mechanisms of the DNA-recognizing enzymes, which have not ever been observed using conventional DNA oligomer composed of just natural nucleobases.
Abstract: This paper is mainly concerned with the application of
a novel technique of data interpretation for classifying measurements
of plasma columns in Tokamak reactors for nuclear fusion
applications. The proposed method exploits several concepts derived
from soft computing theory. In particular, Artificial Neural Networks
and Multi-Class Support Vector Machines have been exploited to
classify magnetic variables useful to determine shape and position of
the plasma with a reduced computational complexity. The proposed
technique is used to analyze simulated databases of plasma equilibria
based on ITER geometry configuration. As well as demonstrating the
successful recovery of scalar equilibrium parameters, we show that
the technique can yield practical advantages compared with earlier
methods.