Abstract: This study demonstrates the use of Class F fly ash in
combination with lime or lime kiln dust in the full depth reclamation
(FDR) of asphalt pavements. FDR, in the context of this paper, is a
process of pulverizing a predetermined amount of flexible pavement
that is structurally deficient, blending it with chemical additives and
water, and compacting it in place to construct a new stabilized base
course. Test sections of two structurally deficient asphalt pavements
were reclaimed using Class F fly ash in combination with lime and
lime kiln dust. In addition, control sections were constructed using
cement, cement and emulsion, lime kiln dust and emulsion, and mill
and fill. The service performance and structural behavior of the FDR
pavement test sections were monitored to determine how the fly ash
sections compared to other more traditional pavement rehabilitation
techniques. Service performance and structural behavior were
determined with the use of sensors embedded in the road and Falling
Weight Deflectometer (FWD) tests. Monitoring results of the FWD
tests conducted up to 2 years after reclamation show that the cement,
fly ash+LKD, and fly ash+lime sections exhibited two year resilient
modulus values comparable to open graded cement stabilized
aggregates (more than 750 ksi). The cement treatment resulted in a
significant increase in resilient modulus within 3 weeks of
construction and beyond this curing time, the stiffness increase was
slow. On the other hand, the fly ash+LKD and fly ash+lime test
sections indicated slower shorter-term increase in stiffness. The fly
ash+LKD and fly ash+lime section average resilient modulus values
at two years after construction were in excess of 800 ksi. Additional
longer-term testing data will be available from ongoing pavement
performance and environmental condition data collection at the two
pavement sites.
Abstract: Cancers could normally be marked by a number of
differentially expressed genes which show enormous potential as
biomarkers for a certain disease. Recent years, cancer classification
based on the investigation of gene expression profiles derived by
high-throughput microarrays has widely been used. The selection of
discriminative genes is, therefore, an essential preprocess step in
carcinogenesis studies. In this paper, we have proposed a novel gene
selector using information-theoretic measures for biological
discovery. This multivariate filter is a four-stage framework through
the analyses of feature relevance, feature interdependence, feature
redundancy-dependence and subset rankings, and having been
examined on the colon cancer data set. Our experimental result show
that the proposed method outperformed other information theorem
based filters in all aspect of classification errors and classification
performance.
Abstract: Current research has explored the impact of
instructional immediacy, defined as those behaviors that help build
close relationships or feelings of closeness, both on cognition and
motivation in the traditional classroom and online classroom;
however, online courses continue to suffer from higher dropout rates.
Based on Albert Bandura-s Social Cognitive Theory, four primary
relationships or interactions in an online course will be explored in
light of how they can provide immediacy thereby reducing student
attrition and improving cognitive learning. The four relationships are
teacher-student, student-student, and student-content, and studentcomputer.
Results of a study conducted with inservice teachers
completing a 14-week online professional development technology
course will be examined to demonstrate immediacy strategies that
improve cognitive learning and reduce student attrition. Results of
the study reveal that students can be motivated through various
interactions and instructional immediacy behaviors which lead to
higher completion rates, improved self-efficacy, and cognitive
learning.
Abstract: Business process model describes process flow of a
business and can be seen as the requirement for developing a
software application. This paper discusses a BPM2CD guideline
which complements the Model Driven Architecture concept by
suggesting how to create a platform-independent software model in
the form of a UML class diagram from a business process model. An
important step is the identification of UML classes from the business
process model. A technique for object-oriented analysis called
domain analysis is borrowed and key concepts in the business
process model will be discovered and proposed as candidate classes
for the class diagram. The paper enhances this step by using ontology
search to help identify important classes for the business domain. As
ontology is a source of knowledge for a particular domain which
itself can link to ontologies of related domains, the search can give a
refined set of candidate classes for the resulting class diagram.
Abstract: Studies in neuroscience suggest that both global and
local feature information are crucial for perception and recognition of
faces. It is widely believed that local feature is less sensitive to
variations caused by illumination, expression and illumination. In
this paper, we target at designing and learning local features for face
recognition. We designed three types of local features. They are
semi-global feature, local patch feature and tangent shape feature.
The designing of semi-global feature aims at taking advantage of
global-like feature and meanwhile avoiding suppressing AdaBoost
algorithm in boosting weak classifies established from small local
patches. The designing of local patch feature targets at automatically
selecting discriminative features, and is thus different with traditional
ways, in which local patches are usually selected manually to cover
the salient facial components. Also, shape feature is considered in
this paper for frontal view face recognition. These features are
selected and combined under the framework of boosting algorithm
and cascade structure. The experimental results demonstrate that the
proposed approach outperforms the standard eigenface method and
Bayesian method. Moreover, the selected local features and
observations in the experiments are enlightening to researches in
local feature design in face recognition.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: This paper proposes a specialized Web robot to automatically collect objectionable Web contents for use in an objectionable Web content classification system, which creates the URL database of objectionable Web contents. It aims at shortening the update period of the DB, increasing the number of URLs in the DB, and enhancing the accuracy of the information in the DB.
Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.
Abstract: Dengue fever is an important human arboviral disease. Outbreaks are now reported quite often from many parts of the world. The number of cases involving pregnant women and infant cases are increasing every year. The illness is often severe and complications may occur. Deaths often occur because of the difficulties in early diagnosis and in the improper management of the diseases. Dengue antibodies from pregnant women are passed on to infants and this protects the infants from dengue infections. Antibodies from the mother are transferred to the fetus when it is still in the womb. In this study, we formulate a mathematical model to describe the transmission of this disease in pregnant women. The model is formulated by dividing the human population into pregnant women and non-pregnant human (men and non-pregnant women). Each class is subdivided into susceptible (S), infectious (I) and recovered (R) subclasses. We apply standard dynamical analysis to our model. Conditions for the local stability of the equilibrium points are given. The numerical simulations are shown. The bifurcation diagrams of our model are discussed. The control of this disease in pregnant women is discussed in terms of the threshold conditions.
Abstract: Versatile dual-mode class-AB CMOS four-quadrant
analog multiplier circuit is presented. The dual translinear loops and
current mirrors are the basic building blocks in realization scheme.
This technique provides; wide dynamic range, wide-bandwidth response
and low power consumption. The major advantages of this
approach are; its has single ended inputs; since its input is dual translinear
loop operate in class-AB mode which make this multiplier
configuration interesting for low-power applications; current multiplying,
voltage multiplying, or current and voltage multiplying can
be obtainable with balanced input. The simulation results of versatile
analog multiplier demonstrate a linearity error of 1.2 %, a -3dB bandwidth
of about 19MHz, a maximum power consumption of 0.46mW,
and temperature compensated. Operation of versatile analog multiplier
was also confirmed through an experiment using CMOS transistor
array.
Abstract: In this work, we improve a previously developed
segmentation scheme aimed at extracting edge information from
speckled images using a maximum likelihood edge detector. The
scheme was based on finding a threshold for the probability density
function of a new kernel defined as the arithmetic mean-to-geometric
mean ratio field over a circular neighborhood set and, in a general
context, is founded on a likelihood random field model (LRFM). The
segmentation algorithm was applied to discriminated speckle areas
obtained using simple elliptic discriminant functions based on
measures of the signal-to-noise ratio with fractional order moments.
A rigorous stochastic analysis was used to derive an exact expression
for the cumulative density function of the probability density
function of the random field. Based on this, an accurate probability
of error was derived and the performance of the scheme was
analysed. The improved segmentation scheme performed well for
both simulated and real images and showed superior results to those
previously obtained using the original LRFM scheme and standard
edge detection methods. In particular, the false alarm probability was
markedly lower than that of the original LRFM method with
oversegmentation artifacts virtually eliminated. The importance of
this work lies in the development of a stochastic-based segmentation,
allowing an accurate quantification of the probability of false
detection. Non visual quantification and misclassification in medical
ultrasound speckled images is relatively new and is of interest to
clinicians.
Abstract: The coalescer process is one of the methods for oily water treatment by increasing the oil droplet size in order to enhance the separating velocity and thus effective separation. However, the presence of surfactants in an oily emulsion can limit the obtained mechanisms due to the small oil size related with stabilized emulsion. In this regard, the purpose of this research is to improve the efficiency of the coalescer process for treating the stabilized emulsion. The effects of bed types, bed height, liquid flow rate and stage coalescer (step-bed) on the treatment efficiencies in term of COD values were studied. Note that the treatment efficiency obtained experimentally was estimated by using the COD values and oil droplet size distribution. The study has shown that the plastic media has more effective to attach with oil particles than the stainless one due to their hydrophobic properties. Furthermore, the suitable bed height (3.5 cm) and step bed (3.5 cm with 2 steps) were necessary in order to well obtain the coalescer performance. The application of step bed coalescer process in reactor has provided the higher treatment efficiencies in term of COD removal than those obtained with classical process. The proposed model for predicting the area under curve and thus treatment efficiency, based on the single collector efficiency (ηT) and the attachment efficiency (α), provides relatively a good coincidence between the experimental and predicted values of treatment efficiencies in this study.
Abstract: In this article, a method has been offered to classify
normal and defective tiles using wavelet transform and artificial
neural networks. The proposed algorithm calculates max and min
medians as well as the standard deviation and average of detail
images obtained from wavelet filters, then comes by feature vectors
and attempts to classify the given tile using a Perceptron neural
network with a single hidden layer. In this study along with the
proposal of using median of optimum points as the basic feature and
its comparison with the rest of the statistical features in the wavelet
field, the relational advantages of Haar wavelet is investigated. This
method has been experimented on a number of various tile designs
and in average, it has been valid for over 90% of the cases. Amongst
the other advantages, high speed and low calculating load are
prominent.
Abstract: This paper describes simple implementation of
homotopy (also called continuation) algorithm for determining the proper resistance of the resistor to dissipate energy at a specified rate of an electric circuit. Homotopy algorithm can be considered as a developing of the classical methods in numerical computing such as Newton-Raphson and fixed
point methods. In homoptopy methods, an embedding
parameter is used to control the convergence. The method purposed in this work utilizes a special homotopy called Newton homotopy. Numerical example solved in MATLAB is given to show the effectiveness of the purposed method
Abstract: In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.
Abstract: This paper presents a generalized formulation for the
problem of buckling optimization of anisotropic, radially graded,
thin-walled, long cylinders subject to external hydrostatic pressure.
The main structure to be analyzed is built of multi-angle fibrous
laminated composite lay-ups having different volume fractions of the
constituent materials within the individual plies. This yield to a
piecewise grading of the material in the radial direction; that is the
physical and mechanical properties of the composite material are
allowed to vary radially. The objective function is measured by
maximizing the critical buckling pressure while preserving the total
structural mass at a constant value equals to that of a baseline
reference design. In the selection of the significant optimization
variables, the fiber volume fractions adjoin the standard design
variables including fiber orientation angles and ply thicknesses. The
mathematical formulation employs the classical lamination theory,
where an analytical solution that accounts for the effective axial and
flexural stiffness separately as well as the inclusion of the coupling
stiffness terms is presented. The proposed model deals with
dimensionless quantities in order to be valid for thin shells having
arbitrary thickness-to-radius ratios. The critical buckling pressure
level curves augmented with the mass equality constraint are given
for several types of cylinders showing the functional dependence of
the constrained objective function on the selected design variables. It
was shown that material grading can have significant contribution to
the whole optimization process in achieving the required structural
designs with enhanced stability limits.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.
Abstract: Whilst there is growing evidence that activity
across the lifespan is beneficial for improved health, there are
also many changes involved with the aging process and
subsequently the potential for reduced indices of health. The
nexus between health, physical activity and aging is complex
and has raised much interest in recent times due to the
realization that a multifaceted approached is necessary in
order to counteract a growing obesity epidemic. By
investigating age based trends within a population adhering to
competitive sport at older ages, further insight might be
gleaned to assist in understanding one of many factors
influencing this relationship.
BMI was derived using data gathered on a total of 6,071
masters athletes (51.9% male, 48.1% female) aged 25 to 91
years ( =51.5, s =±9.7), competing at the Sydney World
Masters Games (2009). Using linear and loess regression it
was demonstrated that the usual tendency for prevalence of
higher BMI increasing with age was reversed in the sample.
This trend in reversal was repeated for both male and female
only sub-sets of the sample participants, indicating the
possibility of improved prevalence of BMI with increasing
age for both the sample as a whole and these individual subgroups.
This evidence of improved classification in one index of
health (reduced BMI) for masters athletes (when compared to
the general population) implies there are either improved
levels of this index of health with aging due to adherence to
sport or possibly the reduced BMI is advantageous and
contributes to this cohort adhering (or being attracted) to
masters sport at older ages. Demonstration of this
proportionately under-investigated World Masters Games
population having an improved relationship between BMI and
increasing age over the general population is of particular
interest in the context of the measures being taken globally to
curb an obesity epidemic.
Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.
Abstract: This paper investigates the problem of designing a robust state-feedback controller for a class of uncertain Markovian jump nonlinear systems that guarantees the L2-gain from an exogenous input to a regulated output is less than or equal to a prescribed value. First, we approximate this class of uncertain Markovian jump nonlinear systems by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, LMI-based sufficient conditions for the uncertain Markovian jump nonlinear systems to have an H performance are derived. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.